AADRL Phase 1

Shajay Bhooshan Studio

Tutors: Shajay Bhooshan Assistants: Alicia Ahmad, Vishu Bhooshan, David Reeves Aydinoglu Begum | Borello Federico | Siedler Philipp

DNA: DISCRETE NETWORK ASSEMBLY

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Phase 1: Index _01 Abstract _01.1 Abstract _02 Introduction _02.1 Thesis Statement _02.2 Background Research _02.3 Architectural Context _02.4 Technological Context _03 Material Research _03.1 Material System _03.2 Proxy Material _03.3 Principal Material _04 Computational Research _04.1 Topology Optimisation _04.2 Manual Geometric Construction _04.3 Algorithmic Geometric Construction _05 Robotic Fabrication _05.1 Robot Experimentation _05.2 Endeffector Design _05.3 Fabrication Sequence _06 Architectural Application _06.1 Introduction _06.2 High Performance Cities _06.3 References

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Chapter 1: Abstract _01.1 Abstract

_01.1

Abstract

This thesis document criticizes current manufacturing processes heavily depending on complex temporary formwork and scaffolding, proposing an integration method of scaffolding as an actuated permanent equal member of the building structure.

Rethinking Scaffolding. Recent rapid development of computer aided design (CAD) tools and computers give designers and architects possibilities to design and simulate complex geometries and building structures.

Constructing this shapes require increasing complex formwork and scaffolding (Image 1. building site Pantheon, Rome), temporary structures to support and hold building elements in place, removed and most likely wasted after building completion1.

Lightweight Structure2. A revisited building sequence also influences and initiates space to rethink the building structure itself. Lightweight structures are of interest for ages. From Antoni Gaudí’s Sagrada Familia, and his hanging chain models to Frei Otto’s tensile tent structures (Image 2. Munich Olympia Stadium, Frei Otto), emphasizing smallest amounts of material used while maintaining performative properties.

Image 1. Construction site of the Pantheon in Rome built by Apollodorus of Damascus in about 126 AD.

Geometric Importance. Current development in digital fabrication methods and growing accessibility by all professions open up new opportunities for architecture in the 21st century. Rapid digital simulation helps us to calculate physical phenomena and predict possible outcome more precise and fast than ever. Advanced geometric thinking becomes increasingly valuable3, especially for economic reasons. Through tools in the genre of computer aided manufacturing (CAM) the gap between advanced geometry in the digital and fabrication in the real world becomes smaller (Image 3.Palazzetto dello Sport, Pier Luigi Nervi) The approach to be presented suggests a topology optimisation process4 with the objective of material reduction. A compression tension algorithm defines positioning of segmented members5 of an integrated spatial network, actuated by a membrane wrapping strategy, replacing conventional formwork and scaffolding, as part of the light weight building structure.

Image 2. Munich Olympic Stadium, Frei Otto and Gunther Behnisch, built in Munich in 1972

1 According to a study from 2011, 80% of the total waste production is created in the construction industry ,of which 1.97% comes from the formwork timber.

2

Frei Otto, Lightweight Principle, Institut für leichte Flächentragwerke (IL), 1998.

3 Corentin Fivet and Denis Zastavni, Robert Maillart’s Key Methods from the Salginatobel Bridge Design Process, Journal of the International Association for Shell and Spatial Structures, 2011.

4 Bendsoe MP, Sigmund O, Topology optimisation: theory, methods and applications.

Springer, Berlin, 2002.

5 Bendsoe MP, Optimal shape design as a material distribution problem. Struct Optim 1(4):193–202, 1989.

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Image 3. Palazzetto dello Sport, Pier Luigi Nervi, built in Piazza Apollodoro in 1957.

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Chapter 2: Introduction _02.1 Thesis Statement _02.2 Background Research _02.3 Architectural Context _02.4 Technological Context

_02.1

Thesis Statement

The project engages topology optimisation1 in architecture, with the objective of material reduction in building structure, while maintaining performative properties.

In the automotive and aeronautic industry, optimisation processes to economize weight and resources are common practice and vital for flying and driving performances. Comparing a tower building and a cars structural elements performance per cubic meter, the building element is exposed to a multitude of stress and at the same time vastly underdeveloped. More money, resources and waste capacity are spent for building construction than anything else in the world, yet nowadays old fashioned building regulations and code tables of isotropic building structure profiles are used and planned with. Topology optimisation (TO) processes for material distribution describe the location of material, but give no manufacturing information. While TO assumes an equal distribution of material mass we suggest to substitute solidity of profile with an advanced geometric approach.

A spatial network of straight segments constructed as an inner scaffold enabling wrapping of the outer membrane2. Skeleton and membrane work together as equal performing members of a hybrid structural system.

The approach suggests to initially keep the purpose of scaffolding, but further in the building sequence, integrates it as a vital member of a hybrid structural system.

MULTI ROBOTIC ASSEMBLY

HYBRID SYSTEM

Instead of constructing a complex temporary scaffold, which after building completion will be removed and possibly wasted, this approach suggests to integrate scaffolding as an equally important member of the building structure. A scaffold not supporting from the outside, like concrete formwork, but spatially independent, growing in any possible way, inside as the structural backbone. This also gives the designer and architect ultimate freedom in construction, able to design an uninterrupted continuously flowing space defining structural surface.

The skeleton and membrane system only works by combination in order to reach a state of load bearing equilibrium between the two members. The skeleton is constructed by a spatial network from discrete elements with circular profiles. Their thickness is relative to the tension and number of layers of the membrane.

A strong node but with flexible segment allows for bending tolerance and gives the system the ability to act like a tensegrity system. Integrating tension by wrapping the flexible inner skeleton, gives the structure strength and stiffness in its final stage.

SELF SUPPORTING NETWORK + MEMBRANE

1 Bendsoe MP, Sigmund O, Topology optimisation: theory, methods and applications.

Springer, Berlin, 2002.

2

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Achim Menges, Robotic woven pavillion, ICD / ITKE Stuttgart, 2015

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Chapter 2: Introduction

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Background Research

Several case studies have been considered to have a better understanding of how to locate the project in the cultural and technological framework of the studio agenda. References from the natural, professional and academic world have been explored in order to identify a coherent approach from digital exploration and conceptualisation to the materialisation of the project. Background research plays a crucial role to identify what already has been done in the field and to inherit knowledge from it, in order to push the research project to a further level of depth and complexity.

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Background Research

Materialising Topology Optimisation: Membrane

Image 5. Topology optimised concrete shell HyperTHREADS 2013, Mexico

Image 6. Trees cocooned in webs after flood in Pakistan Russel Watkins

Image 7. Spatial wrapping Numen

Image 8.

Image 9. Turtle shell structure Natural History Museum, London

Image 10. Turtle spine structure Natural History Museum, London

Image 11. Submarine hull structure Bill Wilson

Image 12. Topology optimised chair. Zaha Hadid Architects (CODE)

Image 13. Complex timber structures Gramazio Kohler Research, ETH Zurich 2012-2017

Image 14. Design and assembly of lightweight metal structures Gramazio Kohler Research, ETH Zurich 2014-2018

Image 15. Robotic weaving ICD / ITKE Stuttgart 2015

Image 16. Fiber reinforcement ICD / ITKE Stuttgart 2014

Materialising Topology Optimisation: Frame

Robotic Fabrication

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_02.3

Architectural Context

DOMINO HOUSE The “Domino House” is a housing diagram designed by architect Le Corbuiser, as a solution to unavoidable housing shortage after World War One. The world “domino” comes from “domus” and “innovation”. It is also referring to the modularity of the dominoes of the similar named board game, as the houses that can be joined from end to end. Prefabricated reinforced concrete was the material for the frame which also could be assembled by non-professionals. Later, Le Corbusier wanted to patent the idea of Maison Domino, with his partner Max Du Bois who owned a concrete company. The idea was an assembly line of houses, like Henry Ford invented for the automobile industry. Eventually, due to lack of backers, Le Corbusier abandoned the patenting and construction idea.

Thus, the diagram remained in the architectural environment and is used all over the world as a role model. The principles of maison domino1 can be observed in following Le Corbusier buildings such as Villa Savoye and Unite d’habitation. The genericness and adaptability of Domino System made it still effective beyond the industrial age. Other than being a positive quality, flexibility-adabtibility became a fundamental ‘mechanism’ for social engineering of spontaneous settlements like Brazilian ‘favelas’ (Image 16) or Greek ‘polykatoikias’ (Image 17) which were as a multistorey apartment building for the Athenian bourgeoisie.2 (Vittorio Aureli, Issaias and Giudici, 2012) In this thesis, we are revisiting the Domino House by extracting the key concepts of the diagram, critically rethinking solutions bearing in mind contemporary technology and social knowledge.

1 Le Corbusier, and Etchells, F., Towards a new architecture. London: Architectural Press., 1946 2 2012

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Vittorio Aureli, P., Issaias, P. and Giudici, M., From Dom-ino to Polykatoikia. Domus,

Chapter 2: Introduction

Image 16. Pre-fabricated Housing Structure in Heliopolis Favela, Sao Paulo (Brazil)

Image 17. ‘Polykatoikias’ in Athens(Greece).

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_02.3

Architectural Context

The Maison Domino has key points that Le Corbusier had declared in his 5 points of architecture1. These principals can also be observed in Le Corbusier’s other housing projects such as Villa Savoye and Unite d’habitation.

_1 Columns(Pilotis) Elevating the mass off the ground _2 Free Plan (Plan Libre) Seperation of the load bearing columns from the walls that subdivides the space. _3 Free facade (Façade Libre) Continuation of free plan in vertical plane

Image 18. Villa Savoye, Le Corbusier, 1931

_4 Ribbon Window (Fenetre de Longerue) Long horizontal sliding window _5 Roof Garden (Toit-Jardin) Restoring the area of ground covered by the house

Image 19. Unite d’habitation, Le Corbusier,1952

1

Le Corbusier, and Etchells, F. (1946). Towards a new architecture. London: Architectural Press.

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_02.3

Architectural Context

The construction system of Maison Domino is designed to minimize the use of material reducing the global weight and cost, to counter the housing demand after the 1914 where one fifth of the Belgian population was homeless. It would have been as an house factory line, like Henry Ford invented one year before for the aumobile industry.

The architectural elements of the Domino diagram reflect Le Corbusier’s five points previuosly mentioned. The pilotis allows to free the facade of the structural function and open up the plan to a more free interpretation without the constraint of structural walls. The staircase is positioned on the side of the module to allow the repetibililty of the latter, both in the horizontal and vertical axis. The innovative elements introduced by Le Corbusier were possible thanks to the use of concrete as material for the entire structure, pilotis, slabs and staircase1.

Le Corbusier had Fordist standardisation in mind and yet produced the perfect architectural symbol for an era obsessed with customisation and participation. Stripped of architecture, the Dom-ino is pure system. It is an invitation to complete it in anyway people prefer.

2.55m 0.3m 2.55m 0.3m

0.3m

Perspective diagram, Maison Domino, Le Corbusier, 1914.

4.1m

4.1m

2.4m

2.55m

4.1m

4.1m

4.1m

2.55m

2.4m

0.8m

0.3m

4.1m 0.3m

0.3m

Vertical elements distribution, Maison Domino, Le Corbusier, 1914.

1 AA School, (2016). [online] Available at: http://www.brettsteele.net/wp-content/ uploads/2014/10/2014-dom-ino-booklet.pdf [Accessed 21 Apr. 2016].

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_02.3

Architectural Context

The Domino House diagram has been criticised according three main aspects: _1 Material Isotropic distribution of the concrete, resulting in structural redundancy. _2 Grid System Rigid grid system which doesn’t allow program flexibility and adaptable configuration.

Material Topology Optimisation (material redistribution)

_3 Surface Absence of surface / enclosure strategy.

Programatic Adaptability Flexible layout

Introducing Surface Hybrid system (structure + surface)

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_02.4

Technological Context

ROBOTIC FABRICATION IN ARCHITECTURE In 1990s, with the impact of increasing use of digital technologies in architecture, abstract virtual worlds came into the architectural scene. Those digital worlds without materiality is exploring the limits of architecture through computer aided design technologies. Introduction of computer controlled processing machines such as milling and laser cutting gave materiality to the abstract dematerialized virtual world.

As the modernist efforts reformulated the transformation of architectural production into a fully automated and rationalized industry, robotic fabrication in architecture became relevant. With the use of robotic technology in architecture, digital technologies are no longer constrained to design, it also becomes practicable for construction. Merging of previously independent domains, robotic technology and materialist actuality of architecture, distinguishes construction automation and general robotisation1.

Robotic fabrication in architecture expanded a new dialogue between design and making, as it leads to high resolution, precision and customisation.

Additionally, it is taking advantage of real-time data flow between design and production by using feedback mechanisms. The term “Digital Materiality in Architecture”2, (Digital materiality in architecture, 2008), sharpened its meaning as the robotic fabrication expands the capacity of the digital world. The very first architectural application of an industrial robot was in 2006, Gantenbein Vineyard Façade in Switzerland (Image 20). Robotic production that ETH developed enabled to lay 20,000 bricks precisely on the desired angle and prescribed intervals. 72 elements which were robotically prefabricated were regularly transported and locally assembled. (Image 21).

Image 20. Gantenbein Vineyard Façade, Gramazio & Kohler, Switzerland, 2006

Image 21. Gantenbein Vineyard Façade Fabrication, Gramazio & Kohler, Switzerland, 2006

1 Fabio Gramazio and Matthias Kohler, The Robotic Touch: How Robots Change Architecture, Research ETH Zurich 2005-2013.

2 Fabio Gramazio and Matthias Kohler, Digital materiality in architecture. Baden: Lars Müller Publishers, 2008.

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Image 22. Complex timber structures Gramazio Kohler Research, ETH Zurich 2012-2017

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_02.4

Technological Context

ADDITIVE MANUFACTURING In terms of manufacturing technology that could be implemented in robotic fabrication, there are additive (∆M1>0), substractive (∆M<0), and formative techniques (∆M = 0), (Chua,Leongi & Lim, 2010). In the subtractive manufacturing processes, raw material, often up to 95 per cent, is removed to achieve the finished component.2 On the contrary, additive processes only uses the material they need to make the part. The history of additive manufacturing (Image 23) is relatively short in comparison with equivalent and subtractive (Image 24) manufacturing. In 1983, the first prototype of a stereolithography (SLA) machine for 3D printing was created by Chuck Hull. Additive fabrication allows complex and performative components out of basic materials. It allows for material customisation and manipulation of the geometry during the fabrication. This adds additional degree of freedom to the process.

Process name

Description

Binder jetting

A liquid bonding agent is selectively deposited to join the powder materials

Directed energy deposition

Focused thermal energy, such as laser, is used to fuse materials by melting as the material are being deposited to form an object

Material extrusion

Materials are heated and selectively dispensed through a nozzle

Material jetting

Materials, such as photopolymers or wax, are selectively dispensed through a nozzle

Powder bed fusion

Thermal energy selectively fuses regions of a powder bed

Sheet lamination

Sheets of materials are cut and stacked to form an objects

Vat photopolymerization

The use of certain types of light, such as ultraviolet light, to selectively solidify liquid photopolymers *

Image 23. Additive Robotic Fabrication of Complex Timber Structures, Zurich, 2012-2017.

Image 24. Subtractive Robotic Fabrication, Hot Wire Cutting. Odico Formworks, Denmark

Source: GAO analysis of ASTM international data *Photopolymer: A light-sensitive polymeric material, especially one used in printing plates or microfilms.

1

∆M refers mass change of the part is positive/negative or equal.

2 Lu, B., Li, D. and Tian, X, Development Trends in Additive Manufacturing and 3D Printing. Engineering, 1(1), pp.085-089, 2015.

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Image 25. Design and assembly of lightweight metal structures Gramazio Kohler Research, ETH Zurich 2014-2018

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Chapter 3: Material Research _03.1 Material System _03.2 Proxy Material _03.3 Principal Material

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Material System

HYBRID SYSTEM In order to achieve a skeleton-membrane integrated system, we used PLA (Polylactic Acid) filaments and LDPE (Low Density Polyetylene) stretch film rolls, as proxy materials.

They are both thermoplast polymers with specific distinct physical properties which are important for our material system. Relatively low melting temperature of PLA makes it easy to cut and deform. Joining PLA without additional adhesive or second material is not as efficient. PLA is quickly overheated and looses some of its flexible properties. Therefor a PLA to PLA joint is brittle and weaker than the original material. Fast cooling time though makes the joint still efficient. Parallel we introduced PCL as a second material to join the PLA rods. PCL is fairly strong and has a very low melting point of about 65°C, but its cooling time is extremely high. For us it still works as a proxy material considering the introduction of robots into the manufacturing process, providing precision and durability.

High tensile strength and elongation property of LDPE stretch film lead us to use the material as wrapping material, the membrane component, taking over tension forces of the PLA structure. One of the main properties of LDPE we are interested in, is the very thin foldable foil we could easily create surfaces from by wrapping, but also diversify strength by repeated wrapping of the material. Friction is increased and is used to enhance the surfaces performance and alter transparency.

After the LDPE membrane is wrapped around the PLA structure, it is shrunk by blowing hot air onto the surface. The LDPE attempts to minimize surface forcing the material to produce higher inner tension and is clinging around the PLA rod skeleton. At the end of this process, the plastic skin provides an imprint of the given geometry.

Finally the PLA-PCL proxy material system will be replaced with 3 mm mild steel rods and metal inert gas (mig) welding as joint strategy. MIG-welding is a welding process where the object to be welded is grounded to the machine, a high load of amperes is used to artificially bypass the electric circuit, producing a very hot spark and melting the steel to be weld-able.

In less than a second the welded spot is cooling down to a temperature maintaining rigidity. Not only cooling and joining processes are extremely fast, but the most beneficial attribute is that the joint becomes stronger than the initial material through welding. This phenomenon is a required material property, complimenting our material system. Strong connecting details are bending resistend and the slightly more bendable rod itself is a necessity to react onto compression forces through wrapping of the LDPE membrane, to become more stable and solid as a whole.

In order to scale up it is important to maintain a constant equilibrium between the two elements, skeleton and skin, which is achieved through a sensibility for profile thickness, geometric assembly and frequency.

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Material System

PROXY-MATERIAL SYSTEM

PRINCIPAL-MATERIAL SYSTEM

1

1

1 Membrane Material: LDPE Price per kg: 1,97 GBP Melting: 95°C Cooling Time: aprx. 24 sec E Modulus: 400 MPA

2

2 Segment Material: PLA Price per kg: 33,30 GBP Melting: 210°C Cooling Time: aprx. 40 sec E Modulus: 500 MPA Energy required: 0.24 kW

3

3 Connection Material: PCL Method: Manual Heatgun Welding Price per kg: 89,70 GBP Melting: 60°C Cooling Time: aprx. 240 sec E Modulus: 470 MPA Energy equired: 0.24 kW

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3

1 Membrane Material: LDPE Price per kg: 1,97 GBP Melting: 95°C Cooling Time: aprx. 24 sec E Modulus: 400 MPA 2 Segment Material: Mild Steel Price per kg: 6,75 GBP Melting: 1400°C Cooling Time: aprx. 0.75 sec E Modulus: 200.000 MPA Energy required: 1.64 kW 3 Connection Material: Copper Electrode Method: MIG-Welding Price per kg: 9,75 GBP Melting: 1083°C Cooling Time: aprx. 1.6 sec E Modulus: 117.000 MPA Energy required: 1.26 kW

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Proxy Material

PRINCIPAL DESCRIPTION

Polymers

The name polymer comes from itâ&#x20AC;&#x2122;s molecular configuration. Many repeated subunits form one molecule or macromolecule. Because of many potential combinations, polymer molecule chains flourish a great variety of properties.

Biologic and synthetic differentiations can be made, which three main branches are defined under the family of polymers: Duroplasts, Thermoplastics and Elastomers. Even though they are all considered polymers the property contrast between them is very high. This becomes a critical differentiation factor when it comes to material behaviour and mechanical properties. Optical properties become easily distinguishable but are not as important for us.

Duroplast

Thermoplast

A common way for differentiation between polymers is by comparing their macromolecular structural combination. Duroplasts macromolecules are connected in a net-like manner, molecules are connected in a rigid ways which does not allow them to freely move. Thermoplastics molecule chains mostly lie next to each other so they can freely move alongside, which allows them to be formed by heating and hardened by cooling.

Elastomeres form sort of nests of molecular connection, by mechanically pulling elastomeres apart they deform, releasing regenerates the old form which the polymer slowly reforms back to.

Duroplast

Thermoplast

Elastomer

Elastomers

Thermoplastic

Thermosetting

Formed under heat / pressure hardened by cooling

Formed under cool temperature hardened by heat / pressure

Multi process-Reusable Recyclable

Single Process-Not Reusable Toxicity

Macromolecule structure of polymers

The most common polymers: Duroplasts: Melamine-Formaldehyde-Resin (MF), Aminoplast (UF) Thermoplastics: Polyethene (PE), Polypropene (PP), Polystyrene (PS), Polyvinyl chloride (PVC), Polyamide (PA), Polymethyl methacrylate (PMMA), Polycaprolactone (PCL), Polylactic Acid (PLA), Low Density Polyethylene (LLDPE) Elastomere: Polyurethane (PUR)

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Proxy Material

POLYLACTIC ACID (PLA) Polylactic Acid or short PLA is used for many applications. It is also extrudable since it is also an thermoplastic, but the melting temperature is higher than the melting temperature of PCL, which makes it more heat resistant. Mostly though PLA is known for it’s 3D-Printing filament application. Temperatures around 200 °C make the thermoplastic almost liquid and also easily operate-able. Even though the operating temperature of PLA is way higher than the operating temperature of PCL the face change, soft to hard, hot to cold is quicker. One of the major disadvantages of PLA compared to PCL is that once the filament has been heated and extruded it becomes very stiff but brittle, while PCL’s reformable cycle is almost infinite. This property was at the end the crucial factor to dismiss PLA as our material to be used for the substructure frame. Also standing in conflict with one of our main objectives to create design process, adaptive and reshape-able throughout time.1 Density: 1.210-1.430 g/cm3 Heat Deflection Temperature: 49 - 52 °C Melting temperature: 150-160 °C Tensile strength: 61 - 66 N/mm2 Solubility in water: Insoluble Toxic Level: Non Toxic

Image 28. PLA granulate palets

-0,1

1st heating Cooling 2nd heating

-0,0

-0,2

Heat Flow, Mg/mg

-0,1

Temperature °C

60

80

100

120

140

160

180

Image 26. Microscopic view and graph of PLA behavior when it is heated after cooling

Image 27. PLA plastic filament 1

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Image 29. Different levels of heated PLA, mycroscopic view x 5000

Walker, A. (n.d.). Plastics: The Building Blocks of the Twentieth Century.

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Proxy Material

POLYCAPROLACTONE Polycaprolactone is mostly used in the medical field. Artificial tissue is engineered, but also orthopaedic moulds are created with PCL. The most interesting upside of PCL is it’s low melting point and easy deform-ability, which make the material unique. Granulate palets can be heated up with warm water and bring the material in seconds to it’s optimal operating temperature. Deformation can be easily achieved by hand.

PCL is very interesting for our project because of it’s well combination with other plastics and low delta of phase change between hard and soft. For especially because the fabrication strategy is not like classical 3D-Printing processes where layer by layer is printed. But an adaptive 3D-printing process, printing in space, with hot material connecting on to cold existing structures.

One of the biggest challenges to work with the material to achieve the highest possible fidelity is to control the temperature extremely precise, so we can distinguish between soft bendable parts and hard supporting nodes as references for new structure.

Density: 1.145 g/cm3 Elastic, elongation: 56% to 93% Melting temperature: 60 °C Tensile strength: 264.8 N/mm2 (melt extruded PCL) Glass transition: -60 °C temperature Toxic Level: Non toxic Combines well with other plastics Easily fabricated

Image 30. PCL water melting process

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Image 31. PCL granulate palets

Image 32. Different levels of heated PCL, mycroscopic view x 20000

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Proxy Material

LOW DENSITY POLYETHYLENE (LDPE) Polycaprolactone is mostly used in the medical field. Artificial tissue is engineered, but also orthopaedic moulds are created with PCL. The most interesting upside of PCL is it’s low melting point and easy deform-ability, which make the material unique. Granulate palets can be heated up with warm water and bring the material in seconds to it’s optimal operating temperature. Deformation can be easily achieved by hand.

PCL is very interesting for our project because of it’s well combination with other plastics and low delta of phase change between hard and soft. For especially because the fabrication strategy is not like classical 3D-Printing processes where layer by layer is printed. But an adaptive 3D-printing process, printing in space, with hot material connecting on to cold existing structures. One of the biggest challenges to work with the material to achieve the highest possible fidelity is to control the temperature extremely precise, so we can distinguish between soft bendable parts and hard supporting nodes as references for new structure.

Density: 0.915 g/cm3 Elongation Value: up to 500% Melting temperature: 120 °C-160 °C Tensile strength: 30 N/mm2 High load capacity Low manufacturing energy use

Image 34. LLDPE granulate palets

Unstretched (35 m) Prestretched (40m) Fully stretched (75m)

Image 33. LLDPE stretch film roll

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Image 35. Different levels of stretched LLDPE, mycroscopic view x 5000

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Principal Material

PRINCIPAL DESCRIPTION

Metal Alloys

In general a metal is an element or alloy, typically hard, obscure and is electrical and thermal conductive. Metals can be mechanically deformed without breaking or cracking1, melted or fused with other elements. A vast majority, 91 of 118 elements, in the periodic table are metals, the others are nonmetals or metalloids.

Metals have higher densities than non-metals, but within metals there is a high range of density, starting from osmium as the densest to lithium which is the least dense metal. Lower density also means low hardness and low melting point.

1 Stress 2 Strain 3 Run 4 Rise 5 Yield Strength 6 Ultimate Strength 7 Strain Hardening 8 Necking 9 Fracture

7

1 5

Nonferrous

Ferrous

8 6 9

Cast irons

4

Steels

3 2 Youngâ&#x20AC;&#x2122;s Modulus = Rise/Run = Slope

Low alloy

High alloy

FCC (face-centered cubic)

Low-carbon

Medium-carbon

High-carbon

HCP (hexagonal close-packed) Macromolecule structure of metals

Plain The most common metals: Base: Aluminium (Al), Copper (Cu), Lead (Pb), Nickel (Ni), Tin (Sn) Precious: Gold (Au), Silver (Ag), Platinum (Pt), Palladium (Pd) Minor: Molybdenum (Mo), Cobalt (Co), Manganese (Mn), Germanium (Ge), Selenium (Se), Silicon (Si)

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High strength low alloy

Plain

Heat treatable

Plain

Tool

Stainless

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Principal Material

MILD STEEL Mild steel is an alloy of iron and a low carbon proportion. It is manufactured with the Bessemer’s process1, which makes it possible, to produce large quantities, the production of the material efficient and thus cheap2. Modern steel making uses iron ore, which is smelted into pig iron inside a blast furnace3.

Iron ore is smelted so the iron is detached, naturally iron has too much carbon to be steel so the carbon value has to be reduced. The reproduction process also allows for other materials to be added and purify the alloy, giving the steel a variation of properties like better conductivity, strength, corrosion protection or aesthetics.

Most steels can be hardened by heat treatment, the most common treatment is called annealing. Recrystallization through heating and cooling extruded mild steel rods, results in reduction of internal stresses and defects. Typically temperatures for the annealing process ranges between 260°C and 760°C. The materials consistency also compliments welding without any further treatment. After the mild steel production process rust prevention may be applied and has to be removed prior to welding. Welding is not only used to connect two pieces of steel, it also has an other useful side effect. MIGwelding produces a high enough temperature to anneal the connection and the steel which is connected, resulting in a stronger connection detail compared to the initial material.

Image 37. Steel casting

Density: 7700 kg/m3 4 Elongation Value: 6 - 7% 5 Melting temperature: 1427 °C 6 Tensile strength: 841 MPA 7

Image 36. Steel profiles

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Image 38. Microscopic view of mild steel, different levels of errosion 200 µm

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Chapter 4: Computational Research _04.1 Topology Optimisation _04.2 Manual Geometrical Construction _04.3 Algorithmic Geomtrical Construction

Computational Research

1752. Giambattista Suardi proposes a geometrical pen for the design of curves from the compound motion of several gears.

1958. Le Corbusier and Iannis Zenakis design the Phillips Pavilion, composed of hyperbolic paraboloids.

1900. Marc Dechevrens invents the campylograph for the production of compound Lissajous curves

1791. George Adams publishes his Geometrical and Graphical Essays, including the first description of Suardi’s machine in English.

1915. William F.Rigge invents the Harmonic Motion machine, one of the most sophisticated instruments for the mechanical production of curves. It makes a general calculation machine for curves.

1799. Gaspard Monge publishes ‘Geometrie descriptive’, a systematic approach to the visual calculation of curves, surfaces and their intersections.

1813. John Farey introduces his mechanical ellipsograph.

1822. Charles Babbage begins work on his difference engine.

1838. C. Protot publishes his Cours special d’architecture, ou Leçons particulieres de geometrie descriptive.

1850. F.C Penrose helicograph.

introduces

1962. Miguel Fisac designs the Laboratorios JORBA, one of the first examples of a new generation of architectural experiments with ruled surfaces.

1962. Desmond Paul Henry exhibits his first computational drawing machine, partially inspired by Suardi’s work.

1963. The DAC-1(Design Augmented by Computer), one of the first systems to use digital visualization for design, is developed by IBM and GM.

his

1871. Edward Burstow, an architect, designs an advanced ellipsograph.

1883. Antoni Gaudi begins his designs for Sagrada Familia, with pervasive use of ruled and projective surfaces.

1884. Hermann Holerith creates a computer based on electromechanical principles, ending the age of mechanical calculation machines.

1800’s

1889. Louis Monduit Publishes his Traite theorique et pratique de stereotomie.

1700’s

1959. Paul de Casteljau describes a type of curve particularly well suited to computation applications. It is essentially the Bezier curve, forerunner of today’s NURBS curves.

1968. The Cybernetic Serendipity exhibition in London gathers those working in design computation, including Desmond Paul Henry and architects using the computer for generative façade patterns.

1997.

Frank Gehry is commissioned with the Guggenheim Museum Bilbao, one of the first buildings to comprehensively use 3D CAD technology from design through assembly.

1900’s

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The Mechanical Prehistory of Design Computation1 1 Andrew Witt, A Machine Epistemology in Architecture, Journal for Architectural Knowledge, 2010.

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The projectâ&#x20AC;&#x2122;s focus on bespoke assembly of discrete elements in space1, requires an intensive use of computational methods, leading to having a precise understanding of the entire workflow from the geometrical organization of the elements to the assembly process.

First of the main methods that have been explored is the topological optimisation algorithm2, with the aim of reduction of the material need in specific structural conditions (load/support) while keeping efficiency and load bearing performances. Multiple iterations of material gradients have been tested in order to build a consistent catalogue of structural behaviours as a framework for the consequent exploration of the fabrication method.

Topology optimisation was meant to criticise the structural inefficiency and redundancy of the Domino House diagram which led to lack in flexibility of the structure itself and of the programatic distribution consequently.

A more efficient distribution of the material, given specific site and structural conditions, was thought as a starting point for a further geometric exploration of the structural diagram.

Load Region High Density Region

Low Density Region Support Region Boundary Region

1 Stefana Parascho, Design and robotic assembly of complex lightweight structures, Architecture and Digital Fabrication, Gramazio & Kohler Research, ETH Zurich, 2014-2018.

2 Bendsoe MP, Sigmund O, Topology optimization: theory, methods and applications.

Springer, Berlin, 2002.

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Density map of a given structural condition which returns a gradient of material distribution within a descrete voxel based environment.

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Topology Optimization Loads Topology optimisation is a mathematical approach that optimizes material layout within a given design space, for a given set of loads and boundary conditions such that the resulting layout meets a prescribed set of performance targets. Topology optimisation has been implemented through the use of finite element methods for the analysis, and optimisation techniques based on the method of moving asymptotes, genetic algorithms, optimality criteria method, level sets, and topological derivatives.

Topology optimisation is used at the concept level of the design process to arrive at a conceptual design proposal that is then fine tuned for performance and manufacturability. This replaces time consuming and costly design iterations and hence reduces design development time and overall cost while improving design performance.

In some cases, proposals from a topology optimisation, although optimal, may be expensive or infeasible to manufacture. These challenges can be overcome through the use of manufacturing constraints in the topology optimisation problem formulation. Using manufacturing constraints, the optimisation yields engineering designs that would satisfy practical manufacturing requirements. In some cases Additive manufacturing technologies are used to manufacture complex optimized shapes that would otherwise need manufacturing constraints. Topology optimisation within architectural design is the use of topological optimisation techniques within the early processes of a building design. This as a tool to convey not only structural coherence, but also aesthetic qualities specific to the morphology of optimised shapes. Topology optimisation offers considerable potential within architectural design as a driver of design innovation and the convergence of the architectural and engineering disciplines.

Topology optimisation allows for the evolution of â&#x20AC;&#x2DC;structural shapeâ&#x20AC;&#x2122;, i.e. shape that simultaneously manifests a structural optimum and an aesthetic assertion. As aesthetic values cannot reasonably be validated through numerical evaluation, only structural criteria can be directly utilized as an objective for an algorithmic optimisation process. But as the optimisation process itself is a linear result of the optimisation algorithm, aesthetic reflections can be indirectly embedded within the calculation by evaluating the initial optimisation output, and then applying adjustments to the model.

Existing optimisation software is developed in preparation for the automotive, aeronautic and naval industries, focusing on the use of isotropic materials with homogeneous compressive and tensile strength properties. The optimisation tools are not specifically developed to meet the design of building structures with use of anisotropic or composite materials such as wood or reinforced concrete.1

1 Per Dombernowsky and Asbjørn Sondergaard, Three-dimensional topology optimisation in architectural and structural design of concrete structures, Aarhus School of Architecture, Denmark

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Area

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Image 39. The topology optimisation process. From an initial set up of design- and non-design space, the optimisation software computes an optimal distribution of material in relation to design criteria.

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Parameters

Load Type: Distributed Support Type:IsoRegion Res: 30x30 Iter: 40 Load Type: Distributed 30% 0.055 Iso: 0.055 Support Type: Region Res: 30x30 30 Iter: 40 Iterations Iso: 0.055 Target %

34%

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0.055 Load Type: Distributed Support Type: Region Res: 30x30 20 Iter: 40 Load Type: Distributed Iterations Iso: 0.055Type: Region Support 35% Res: 30x30 Thickness Iter: 40 31% Self WeigthIso: 0.055 75%

Topology Optimisation Output Mesh_02

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Iso

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Load Type: Distributed Support Type: Region 30 Res: 30x30 Iterations Iter: 40 Iso: 0.055 33%

Topology Optimisation Output Mesh_01

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Load Type: Distributed Support Type: Region Res: 30x30 Target % Iter: 40 Iso: 0.055 60%

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Load Type: Distributed Support Type: Region 50 Res: 30x30 Iter:Iterations 40 Iso: 0.055 37% Thickness

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Structure type: Outer - frame Boundary Elements: Profiles Connection type: Void / square to square # of connection nodes: 4 + 4 = 8

Structure type: Inside- skeleton Boundary Elements: Plates Connection type: Solid / square to square # of connection nodes: 4 + 4 + 4 = 12

Structure type: Inside - skeleton Boundary elements: Plates Connection type: Solid/ square to square # of connection nodes: 4 + 4 + 4 = 12 Structure type: Inside - skeleton Boundary elements: Plates Connection type: Solid/ square to square # of connection nodes: 4 + 4 + 4 = 12

Surface Generation, Wrapping _ 01

Surface Generation, Wrapping _ 03

Structure type: Outer - frame Boundary Elements: Profiles Connection type: Void / square to square # of connection nodes: 4 + 4 + 4 = 12

Structure type: Inside - skeleton Boundary Elements: Profiles Connection type: Void / rectangle to square # of connection nodes: 6+ 4 = 12

Structure type: Inside - skeleton Boundary elements: Profiles Connection type: Void / rectangle to square Structure type: Inside -nodes: skeleton # of connection 6 + 4 = 10 Boundary elements: Profiles Connection type: Void / rectangle to square # of connection nodes: 6 + 4 = 10

Surface Generation, Wrapping _ 02

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Structure type: Inside - skeleton Boundary Elements: Profiles Connection type: Linear / 90 degree shift # of connection nodes: 4 + 2 + 2 + 2 + 2 = 12

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In the existing Domino House Diagram, material use could be optimized through a performative equilibrium between column positioning, column profile thickness and slab thickness. In order to achieve this, we applied topology optimisation algorithms to the Domino House diagram, with refinement process of the rough mesh output, modelling and relaxation.

Target %

Iso

30%

0.055

Mesh output

40 Iterations 33% Thickness 33% Self Weigth

Poly modeling

Dynamic relaxation Topology optimisation of the Domino House and dynamic relaxation.

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REINFORCEMENT To add an other layer of complexity with the purpose of local reinforcement we decided to use the topology optimisation model and the extract isocurves. Overall strategy for the reinforcement as an micro pattern on the surface in a self intersecting manner to increase friction forces and gain additional stability.

The Reinforcement as a second layer of hierarchy in the building sequence, but also in terms of structural performance, carrying itself two layers of complexity: First varying between high and low density and second to differentiate the thickness of each reinforcement rod.

The following pages show individual experiments, executing the described reinforcement strategy in different load cases, according to coherent support positions and loads.

The objective is to use only a limited amount of material, to counter appearing stresses and moments within the structure. Therefore we might decide to only use one of the approaches of reinforcement variable.

Target %

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30 Iterations 30% Thickness 30% Self Weight Subdivision Length

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Time

Target %

Time

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Self Weight

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Subdivision Length

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Time

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Profile thickness of different nodes of the structure.

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Membrane wrapping sequence

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Membrane reinforcement experiment sequence

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Coated Cotton Wires

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Surface Stiffness

Shrinking

Surface Stiffness

Shrinking

Surface Stiffness

Shrinking

Surface Stiffness

Shrinking

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MANUAL GEOMETRIC CONSTRUCTION

Manual Geometric Construction: Tubular Branching

In a structural system which is consisting of multiple segments, strength and durability is mostly depending on: length of segments, number of segments, number of nodes and node valencies. To achieve a working segmented structural system, manual modelling of the projected primitive geometry is crucial. Nodal connections, branching and accumulation is understood while manually modelling and geometries are later used as an input in several optimisation algorithms. The feedback mechanism between manual and algorithmic modelling is crucial in order to achieve the most controlled balanced system. This work flow has been followed during the computational experiments and concluded with physical prototypes.

In the tubular branching method, there are three layers of structure. As first layer works as a hollow core, second layer increases surface area and inner volume by perpendicular branching from the core. Lastly, open ended nodes of perpendicular branches are connected with elements of third layer, horizontally or diagonally, depending on the structural situation.

Tubular branching method is important to understand how to achieve the strongest and lightest structure by changing number of segments, length of the segments and their orientation. To investigate further, we have developed a series of catalogues consisting of several structural families.

This digital catalogues were used to choose the most efficient structure and build up a physical series upon them.

There are three different structural families. First one, uses lines as constrains by having a non continuous reinforcement layer. Second family uses meshes as constrains by having a continuous reinforcement layer.

This makes the structure stronger and more durable. Lastly, third family reduces the resolution by decreasing number of edges in the first layer, the hollow core. As this makes the structure lighter, the strength is lower than the high resolution examples.

Manual Geometric Modeling feedback

Layer 1_Hollow core

Algortithmic Geometric Modeling

Layer 2_Increasing volume Layer 3_Reinforcing

Physical Prototyping

Diagram of the work flow utilised within the computational research

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Layering system of the structural network

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CATALOGUE 1 LINES AS CONSTRAINS Total Length of Segments Total Number of Nodes Number + Length of Segments Valency of Nodes Physically Build

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CATALOGUE 2 MESHES AS CONSTRAINS Total Length of Segments Total Number of Nodes Number + Length of Segments Valency of Nodes Physically Build

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CATALOGUE 3 REDUCED RESOLUTION Total Length of Segments Total Number of Nodes Number + Length of Segments Valency of Nodes Physically Build

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1-1 PROTOTYPING As a further experiment of the tube in tube/tubular branching approach, we built a 1-1 scale prototype with the same logic. We used an input geometry which we have produced in our past topology optimisation studies. Building a 1-1 scale prototype of a part of the geometry was important in order to see the difficulties that we face in terms of, both geometrical conditions and fabrication constrains.

Part of the geometry that has been built

Surfacing the wireframe model

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Manual Geometric Construction

MANUAL GEOMETRIC CONSTRUCTION: CATENARY GEOMETRY A preliminary set of experiments have been conducted concerning the build up of catenary geometries due to their optimal performances under compression loads and their material efficiency in order to minimise external supports. Manual modelling experiments led to a clear understanding of catenaries geometrical features as a base field for further algorithmic explorations.

Connecting parabolas to an imaginary boundary increases the inner volume and creates a surface area for surfacing strategies. As the length of the boundary connections determines the hollowness of the overall system, branching on tips of the boundary sets the strength of the surface connection areas.

Parabola Focus Points BOUNDARY BOUNDARY BOUNDARYTOPOLOGY TOPOLOGY TOPOLOGY

POLOGY RY TOPOLOGY

OFFSET BOUNDARY OFFSET BOUNDARY

REFERENCE PLANES REFERENCE PLANES

PARABOLA FOCUS POINTS PARABOLA FOCUS POINTS

OFFSET OFFSET OFFSETBOUNDARY BOUNDARY BOUNDARY

REFERENCE REFERENCE REFERENCEPLANES PLANES PLANES

INTERSECTING PARABOLAS INTERSECTING PARABOLAS

PARABOLA PARABOLA PARABOLAFOCUS FOCUS FOCUSPOINTS POINTS POINTS

SEGMENTING PARABOLAS SEGMENTING PARABOLAS

Reference Planes

Offset Boundary

Boundary topology BOUNDARY BOUNDARY TOPOLOGY TOPOLOGY

OFFSET OFFSET BOUNDARY BOUNDARY

REFERENCE REFERENCE PLANES PLANES

Intersecting Parabolas INTERSECTING INTERSECTING INTERSECTINGPARABOLAS PARABOLAS PARABOLAS

Boundary Connection BOUNDARY CONNECTION BOUNDARY CONNECTION

PARABOLA PARABOLA FOCUS FOCUS POINTS POINTS

Segmenting Parabolas SEGMENTING SEGMENTING SEGMENTINGPARABOLAS PARABOLAS PARABOLAS

BOUNDARY BOUNDARY BOUNDARYCONNECTIO CONNECTIO CONNECTIO

Network NETWORK TO OPTIMIZE NETWORK TO OPTIMIZE

Manual catenary construction process.

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ALGORITHMIC GEOMETRIC CONSTRUCTION The topological optimisation algorithm returns detailed informations about the distribution in space of matter but lacks in how the given geometry would be materialised. A further step of the computational process has been to explore different methods to build up a structurally performing architectural element given a rough mesh output. A geometry driven approach has been considered, in which precise positioning of discrete elements in space work as internal self supporting structural skeleton of the given volume, and as supporting members of the external membrane.

Multiple algorithms have been researched and the output catalogued to generate topologies, able to fit the performance criteria requested and to minimize the use of external supporting structures.

Current state of supporting structures calculated with 3d printing software.

Algorithmic method explored to internalise supporting structures.

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TOPOLOGY RESEARCH: SPHERICAL PACKING - NEAREST NEIGHBOUR The spherical packing method1 has been utilised to achieve an equally distributed network within the boundary volume. Single and multiple diameters have been thought to differentiate the network resolution and give hierarchy to the structural members. A tetrahedral based topology was the result of the majority of the iterations.

Neighbour distance based connectivity has been also explored to generate connecting members between the sphere locations and the outer surface as a prototypical connective system.

Spherical Packing

1 Adam B. Hopkins and Frank H. Stillinger, Densest local sphere-packing diversity: General concepts and application to two dimensions, Princeton University, Princeton, USA 2010.

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Tetrahedral network generated through spherical packing method.

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TOPOLOGY RESEARCH: TOPOLOGICAL OPERATORS + PHYSICS An interactive and physic based method has been researched to generate topologies and connectivity diagrams of nodes and bars. The computation method utilised was organised into two main aspects: topological operators and physics1.

Topological operators modifies the topology (connectivity between nodes) manipulating the number of nodes and members according to determine criteria like nodes valence, distance between nodes, distance nodeboundary geometry. A force based calculation has been also integrated to give the model the ability to self organise according to physic behaviours like gravity, repulsion forces between nodes and particle-spring forces.

Multiple setup conditions have been analysed as well as different boundary geometries, starting from euclidean primitives to the former topological optimised results.

Restitution Lenght

+F

P2

Î&#x201D;P

Gravity Force

Repulsion Force

3 Nodes + 2 connections

-F

P1

g

Single Node

Î&#x201D;P

Boundary Repulsion Force

4 Nodes 4 connections

g

radiu

s

Particle-Spring

Closest Boundary Connectivity

Boundary Snapping

Valence 3 Connectivity

Nodes Snapping within Threshold

Nearest neighbour

1 Axel Kilian and John Ochsendorf, Particle-spring systems for structural form finding, Journal of the International Association for Shell and Spatial Structures: IASS, Vol.46, 2005.

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Topological operators and forces involved in the computational model.

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Spring

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Network topology generated within a spherical and rectangular boundary geometry.

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Details of a network structure generated from the topology optimised result.

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TOPOLOGY RESEARCH: SPACE COLONISATION The space colonisation algorithm1 has been researched and utilised to generate tree like structures within a given geometry. The recursive based logic allowed to modify the growth criteria based on different parameters like length and number of branches, stimulant location and free space search.

The algorithm performed a local-by index research to optimise the computation speed and output vertical lines starting from the naked nodes as a notation system for potential supporting structures.

Attraction Force

Attractor Point

New Branch

Existing Branch

1 Adam Runions, Brendan Lane, and Przemyslaw Prusinkiewicz, Modeling Trees with a Space Colonization Algorithm, University of Calgary, Canada, 2007.

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Network structures generated through space colonisation algorithm.

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TOPOLOGY RESEARCH: TOPOLOGY RELAXATION The topologies generated with the methods previously explained have been utilised as input of two different relaxation algorithms: force density method1 and dynamic relaxation method2.

Iter 0

The force density method has been utilised to explore particle-spring systems to find structural forms composed of only axial forces.

The equilibrium state of each particle is researched through the use of the Euler integration method3 which allowed to interactively operate while the simulation was running. This method allowed us to computationally manipulate in real time the structural conditions of our simulation and generate multiple iterations of structural coherent design outcomes.

Given the force density as the ratio of force to length in each member of the structure, the iso-tension graph has been computed, a resultant compression only graph in which the force density in each member is the same.

The best-fit graph has also been computed as a resultant compressiononly graph where the force-density in each member is calculated such that the deviation of the resulting graph is as minimal as possible from the input graph.

Iter 1

The dynamic relaxation method has been explored which is based on discretising the continuum by placing the mass at nodes (particles) and defining the relationship between nodes in terms of stiffness. External loads are applied to alter the system towards the equilibrium state. The geometry is updated dynamically through an iterative process in which the relationship between the residual forces and the geometry itself and viceversa is calculated.

Force Density = F / L F

F

L

Iter 2

1 Schek, H.J, Force Density Methods for Form Finding and Computation of General Networks, Computer Methods in Applied Mechanics and Engineering, 1974.

2 L. C. Zhang, M. Kadkhodayan, Y.-W. Mai, Development of the maDR method, Centre for Advanced Materials Technology, University of Sidney, Australia, 1993.

3 Butcher, John C., Numerical Methods for Ordinary Differential Equations, New York, John Wiley & Sons, 2003.

Iterative growth process of the three like structure with neighbour check notation.

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Iter 0

Fixed Nodes

Compression Members Iter 1

Free Nodes

Iter 2

Compression only graph with minimal deviation from the given graph according to the given loads and self weight.

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Iter 0

Iter 1

Iter 0

Iter 1

Iter 0

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Resultant compression-only graph where the force density in each member is the same i.e the ratio of force to length in each member is the same.

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Compression only graphs computed through dynamic relaxation method.

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Outline of the Domino House skeletal geometry used as an input to same algortihm.

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The dynamic relaxation method has been applied further to the networks obtained through the topology optimisation process in order to achieve only compression graphs.

The output networks have been used as guidelines for prototypical architectural elements which have been manually modelled and optimised for 3d printing.

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References

Bespoke Digital Fabrication

Additive Fabrication

Carbon Fiber Wiring

Design and assembly of lightweight metal structures Gramazio Kohler Research, ETH Zurich 2014-2018

Additive Robotic Fabrication of Complex Timber Structures, Zurich, 2012-2017.

ICD/ITKE, Pavilion, Stuttgart, 2014.

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Robot Experimentation

The spread of multi-functional industrial robots has had exponential acceleration in the last decade that it has become a standard tool in many industries, where automation, efficiency and accuracy are the heart of the production process. Since the â&#x20AC;&#x2DC;80s, with the development of information and computational technologies, the machines begin to be controlled through digital tools defining the beginning of a new paradigm in which the virtual world and the physical world come together and influence each other, drawing the foundations for the emergence of new processes and production strategies.

The development of these technologies does not occur evenly in all disciplines and production companies but condenses on specific sectors, such as automotive and aerospace, going to redefine the quality standards of their respective sectors. The birth of these machines is the result of a need of greater control over the production process and greater automation, elements not necessarily connected with the world of construction and architecture. The diffusion of industrial robots in these fields occurs in manners much slower and gradual as for digitization, because of an industry in which the development timings and implementation medium assume different sizes and durations, a much more fragmented and complex industry. Very significant reason is also a limited and wellestablished design methodology and dominant constructive that has not evolved with the same speed of other disciplines; in defence of this, it is the fact that the architectural project encloses a more difficult to calculate or in some cases of not calculable variable number that can not be foreseen in advance and then inserted in a fully automated process, but they provide, at least for now, the presence of the human factor as an element of conjunction with the real world, able to use intuition and take charge of the major design decisions1.

Despite a more gradual evolution of the last decade we have seen a substantial increase in the use of robot technology both within the construction process and within the design phase. The flexibility of robots such as industrial arms provides a wide spectrum of potential uses, not limiting them to unique automation tools predetermined and finite processes, but added elements able to expand the possibilities of the designer.

1 Fabio Gramazio and Matthias Kohler, The Robotic Touch: How Robots Change Architecture, Research ETH Zurich 2005-2013.

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Location: ODICO Formworks, Odense (DK) Robot: ABB IRB 6620 (suspended) Objectives: Main objective of the workshop was to test the behaviour of polymorph plastic through the use of a custom made extruder connected to an ABB robot. Testing was aimed to achieve both linear and spatial geometrical configurations and patterns. The workflow has been set up in this way: robot path definition via CAD software, translation into G-Code via in house software PyRabbit, manual or automated execution of the code.

Preliminary Phase: Manual control of the robot with linear and spatial extrusions generating catenary geometries as result of the material behaviour and extrusion strategy.

Automated Phase: Automation of the extrusion process and calibration of printing speed according to the material behaviour.

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The robot setup at ODICO in Odense, Danmark gave us great opportunity to experiment with material behaviour and execute first robot movement tests in jogging mode but also autonomous via G-Code.

We rapidly realized that we would not need to move the endeffector in a catenary way to creat arches, but rather move it in a two dimensional manner in the XY-Plane. In that way we can use the materials flowing behaviour through gravity, let the extruded PCL drip and let time form an catenary by itself, as an form-finding, not an form describing process.

Initialization

Path Configuration

PERS tooldata hotwire := [ TRUE, [ [ 0.000, 0.000, 567.700 ], [ 1.000000000, 0.000000000, 0.000000000, 0.000000000 ] ], [ 2.0, [ 0.010, 0.000, 567.700 ], [ 1, 0, 0, 0 ], 0, 0, 0 ] ]; PERS pos Pos_Offset := [ 0, 0, 0 ]; PERS speeddata cur_vel := [ 200.0, 200.0, 1000.0, 1000.0 ]; PERS zonedata cur_zone := [FALSE, 1, 1, 250, 1, 200, 1]; PERS wobjdata cur_wobj := [ FALSE, TRUE, “”, [ [ 0.000, 0.000, -3210.000 ], [ 1.000000000, 0.000000000, 0.000000000, 0.000000000 ] ], [ [0, 0, 0], [1, 0, 0 ,0] ] ]; ! File generated: 2-4-2016 at 10:0 ! >>> start config details ! PyRAPID version: 0.22.i-WIP-no-x11 ! generated for robot model: irb6620_22_150_m2004_wallmounted ! <<< end config details

Variation in time, frequency of stops and movement across supports gave us insight into material behaviour. Throughout the time at ODICO we increased supports, starting with a single linear support, through a second parallel and finally three linear support beams forming a triangle, adding degree of complexity to the extruded catenary dripping process.

PROC main() ! >>> start header ! crank up the path resolution to increase toolpath precision PathResol 50; AccSet 100, 50; ! <<< end header ! >>> start procedure_on ! <<< end procedure_on ! runs the tooling path run; ! >>> start procedure_off ! <<< end procedure_off ! >>> start footer ! <<< end footer ENDPROC

3s 3s 3s 3s 3s 3s 3s

File Reading

3s 3s 3s

! generated from CAD files: base geometry.stp ! CAD file: base geometry.stp ! CAD file: 0 out of 1, file has 2 shells ! shell name: shell0001 PROC shell0001() ! <<< group name: grouped_faces_000 >>> ! ruled surface 000 MoveL [[1185.0000, 0.0032, 1158.8168],[0.00000000, 0.70710678, 0.70710678, -0.00000000],[0, 0, 0, 1],[0,9E9,9E9,9E9,9E9,9E9]], cur_vel, cur_zone, hotwire \ Wobj:=cur_wobj; ENDPROC

Speed

C°

Heigth TCP

90C°

1cm

20%

Waiting Time

End Procedure

PROC run() ! >>> start pre_toolpath ConfJ \Off; ConfL \Off; ! <<< end pre_toolpath shell0001; shell0002; ! >>> start post_toolpath ConfJ \On; ConfL \On; ! <<< end post_toolpath TPWrite “Cut completed”; Stop; ENDPROC ENDMODULE

PyRabbit code to control and execute G-Code

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Coded Robot Path Catenary Material Formation

4 3

1 2

4 3

1 2 8

5 6

Speed

7

C°

Heigth TCP

90C°

1cm

20%

3s Waiting Time

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Robot Experimentation

Location: University of Innsbruck Exparch-Hochbau, (AU).

Robot: ABB Agilus Objectives: Main aim of the workshop was to print a designed piece that was geometrically, structurally or systematically related with the studio project, by using the facilities of REX LAB (Robotic Experimentation Laboratory).

One of the 3 ABB Robots was used to print concrete with a custom made end effector developed by Hochbau department. The setup consisted in a pump mixing cement and water, transporting the aggregate to the enedeffector and a smaller pump to inject a chemical eccelerator. The calibration of robot speed, endeffector deposition speed, material consistency resulted in a delicate operation.

First Session: Introduction and Tests Introduction to the software that is used to control the robots and setting up the workflow, from C-Code to G-Code.

Second Session: Printing Robot path check in Robot Studio, sequence check with the robot and final deposition of material.

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Robot Experimentation

Two different approaches have been explored to generate the geometry and robot path file; the output files have been tested with a custom robot simulation developed in C++. These two methods were both tested and their output robot path files were used to generate the input G-Code for the robot control.

1 Model and path generated through C++ code.

2 Model definition in Maya and path file preparation through python scripts.

The model has been firstly sliced with a 0.75 layer heigth and then the coordinates exported in .txt file format.

3 The output file of the two approaches has been input in the robot simulation to generate G-Code.

4 Robot Studio has been used to read the G-Code, test robot motion, clash detection and execution.

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void draw() {

...

{

//draw parabola points for (int i = 0; i < parabolaPointNumber; i++) float xp = i;//defining x value float a = -0.6; //opening and direction through sign float h = 5; //z position of extreme float k = 20; //x position of extreme float yp = a*pow(xp - h, 2) + k; //catenary equation

parabolaVector[i] = vec(xp, 0, yp) ;//storing parabola vector points in array }

for (int i = 0; i < parabolaPointNumber; i++)

//loop through parabola points {

for (int j = 0; j < pointNumber; j++) //draw circle segment points around parabola points { float radius = 2; //define circle radius float x = radius * sin(2 * PI / pointNumber * j); //insert into circle equation to extract x float y = radius * cos(2 * PI / pointNumber * j); //insert into circle equation to extract y resultingVector[i][j] = parabolaVector[i] + vec(x, y, 0); //storing circle segment point vector into 2d-array glPointSize(5); //defining point size glColor3f(0.1921, 0.6588, 0.8705); //defining point color drawPoint(resultingVector[i][j]); //draw point } }

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C++

Maya

_Model

_Points generation

_Model

_Slicing (Python Script)

_Points Generation (Python Script)

P (0,0,0)

Points generated through C++ code

Position Coordinates

0,2,5,0,0,-1 1.17557,1.61803,5,0,0,-1 1.90211,0.618034,5,0,0,-1 1.90211,-0.618034,5,0,0,-1 1.17557,-1.61803,5,0,0,-1 2.44929e-016,-2,5,0,0,-1 -1.17557,-1.61803,5,0,0,-1 -1.90211,-0.618034,5,0,0,-1 -1.90211,0.618034,5,0,0,-1 -1.17557,1.61803,5,0,0,-1 1,2,10.4,0,0,-1 2.17557,1.61803,10.4,0,0,-1 2.90211,0.618034,10.4,0,0,-1 2.90211,-0.618034,10.4,0,0,-1 2.17557,-1.61803,10.4,0,0,-1 1,-2,10.4,0,0,-1 -0.17557,-1.61803,10.4,0,0,-1 -0.902113,-0.618034,10.4,0,0,-1 -0.902113,0.618034,10.4,0,0,-1

Point position coordinates and orientation / rotation coordinates

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Rotation Coordinates

0

12

80

Maya robot setup: slicing and points exporting through Python scripts

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2 Slicing Geometry

3 Generating Robot Path _Maya Python Code:

_Maya python code:

_Select 3 vertices of a reference plane and execute _Select the geometry and execute _Layer Height: 0.75 cm

_Select output curves from sliced object and exectute _ Path.txt

import maya.cmds as mc def createPath(curves, layerSwitchDist): linePnts = [] endID = 0 # add rest of layers for i in range(0, len(curves)-1): endPnt = om.MVector(*mc.pointPosition(curves[0]+”.cv[“+str(endID)+”]”))

Sliced geometry

import maya.cmds as mc # define reference plane via selection of 3 vertices #normalDistToPlane(om.MVector(0,0,0), om.MVector(0,0,1), om.MVector(5,7,3)) vtxList = mc.ls(sl=True, fl=True) refPlane = getPlaneFromPoints(vtxList) # select poly to slice and execute polyName = mc.ls(sl=True, fl=True)[0] vertices = mc.ls(mc.polyListComponentConversion(polyName, tv=True), fl=True)

Part of Python code that generates layers

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# get closest cv t_numCv = mc.getAttr(curves[i]+”.spans”) + mc.getAttr(curves[i]+”. degree”) n_numCv = mc.getAttr(curves[i+1]+”.spans”) + mc.getAttr(curves[i]+”. degree”) closDist = 1000000000 closId = 0 for j in range(0, n_numCv-1): tmpPnt = om.MVector(*mc.pointPosition(curves[i]+”.cv[“+str(j)+”]”)) tmpDist = (endPnt-tmpPnt).length() if tmpDist < closDist: closDist = tmpDist closId = j # add curvepnts in order tmpId = closId for j in range(0, t_numCv-1): linePnts.append( om.MVector(*mc.pointPosition(curves[i]+”. cv[“+str(tmpId)+”]”)) ) tmpId += 1 if tmpId >= t_numCv-1: tmpId = 0

Part of Python code that generates the path

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_Importing Path(.txt) to .cpp File _Simulating Robot // adjust robot config thisR.Bars[0].d = 44.5; thisR.Bars[0].a = 15.0; thisR.Bars[0].alpha = -90 * DEG_TO_RAD; thisR.Bars[0].theta = 0; thisR.Bars[1].d = 0; thisR.Bars[1].a = 70.0; thisR.Bars[1].alpha = 0; thisR.Bars[1].theta = -90 * DEG_TO_RAD; thisR.Bars[2].d = 0; thisR.Bars[2].a = 11.5; thisR.Bars[2].alpha = -90 * DEG_TO_RAD; thisR.Bars[2].theta = 0; thisR.Bars[3].d = 79.5; thisR.Bars[3].a = 0; thisR.Bars[3].alpha = 90 * DEG_TO_RAD; thisR.Bars[3].theta = 0; thisR.Bars[4].d = 0; thisR.Bars[4].a = 0; thisR.Bars[4].alpha = -90 * DEG_TO_RAD; thisR.Bars[4].theta = 0; thisR.Bars[5].d = 8.5; thisR.Bars[5].a = 0; thisR.Bars[5].alpha = 0; thisR.Bars[5].theta = 180 * DEG_TO_RAD; thisR.home_rot[0] = 0; thisR.rot[0] = 0; thisR.home_rot[1] = -90; thisR.rot[1] = 0; thisR.home_rot[2] = 0; thisR.rot[2] = 0; thisR.home_rot[3] = 0; thisR.rot[3] = 0; thisR.home_rot[4] = 30; thisR.rot[4] = 0; thisR.home_rot[5] = 180; thisR.rot[5] = 0; cTime = 0; eTime = 1; dTime = 0.1; ...

C++ code to instruct the robot to check for points reachability

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EXPORTING ROBOT TASK FILE (.TXT)

INVERSE KINEMATIC DIAGRAM WITH JOINTS POSITION

Robot Studio simulation and execution // create file ofstream myfile_write; myfile_write.open(“data/robotTask” + to_string(_fileID) + “.txt”, ios::out); if (myfile_write.fail()) cout << “ error in opening file “ << “ABB_OUT” << endl; if (_active == true) // check if active or not {

if (_syncMod == true) // check if sync or not

tcpPos.z * 10 + _offset.z << “], [“;

else //JOINTMOVEMENT { myfile_write << “MoveAbsJ [[“

+ to_string(thisR.rot[0]) + “, “

+ to_string(thisR.rot[1]) + “, “

+ to_string(thisR.rot[2]) + “, “

+ to_string(thisR.rot[3]) + “, “

+ to_string(thisR.rot[4]) + “, “

+ to_string(thisR.rot[5]) + „], [0, 9E9, 9E9, 9E9, 9E9, 9E9]] „; myfile_write << “, Work, z1, Tool0;” << endl; // \WObj : = WObj0; “ << endl; endl;

Transformation Matrices, Joint Configurations

MODULE MainModule ! ============= DECLARATIONS ============ VAR speeddata Work := [150, 100, 5000, 1000]; ! == == == == == == == PROCEDURES == == == == == == = PROC Main() ConfJ \Off; ConfL \Off; MoveL [[879.48, 19.0211, 297.2], [-2.3296e-008, -0.707107, -2.32641e-008, 0.707107], [0, 0, -1, 1], [0, 9E9, 9E9, 9E9, 9E9, 9E9]], Work, z1, Tool0; MoveL [[857.12, 11.7557, 297.2], [-3.9204e-008, -0.707107, -3.83594e-008, 0.707107], [0, 0, -1, 1], [0, 9E9, 9E9, 9E9, 9E9, 9E9]], Work, z1, Tool0; MoveL [[857.12, -11.7557, 297.2], [3.99037e-008, -0.707107, 3.92649e-008, 0.707107], [-1, -1, 0, 1], [0, 9E9, 9E9, 9E9, 9E9, 9E9]], Work, z1, Tool0; MoveL [[879.48, -19.0211, 297.2], [2.3296e-008, -0.707107, 2.32641e-008, 0.707107], [-1, -1, 0, 1], [0, 9E9, 9E9, 9E9, 9E9, 9E9]], Work, z1, Tool0; MoveL [[1393.3, 5.96908e-014, 297.2], [-3.09086e-008, -0.707107, -3.09086e-008, 0.707107], [0, 0, 0, 1], [0, 9E9, 9E9, 9E9, 9E9, 9E9]], Work, z1, Tool0; G-Code export

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DIGITAL WORKFLOW

WORKFLOW Model

Points Coordinates

1 Model

DIGITAL

MAYA 2 Robot Path Path File

Text File

Robot Simulation

Points Coordinates

3 G-Code

PHYSICAL

4 Robot Calibration

5 Material Setup C++ 6 Fabrication G-Code

Text File

ROBOT STUDIO

Execution

Digital workflow from model definition to G-Code export

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Rex-Lab workspace

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Concrete printing end-effector

Chapter 5: Robotic Fabrication

Catenary model concrete printing

Resolution / Texture

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Endeffector Design

ENDEFFECTOR 1.0 4-M5 depth 7 (P.C.D.60)

221.4

198

110.7

73 83

50 45

G1

190.5 160

618 806

228 266 723 912

160

495 ａ 646

189.5

165

95.5 ±0.1

94

94

345

160

95.5 ±0.1

198

95.5±0.1

330 410

1249 1602

190.5 160

4-φ 11

189.5

G6

14

G2

340 440

73

G5

23

φ 23 (Diameter (P.C.D.60) of wire hole)

95.5±0.1

G4

Wire hole (same as opposite side) φ 72 h7

Nachi MZ07

4-φ 11

2-φ 5 H7 depth 7 (P.C.D.60)

e nce am re Fr er fe int dius ra 150 R

170°

G3

4-M5 depth 7 (P.C.D.60)

φ 45 h7

φ 72 h7

φ 45 h7

Next stage of the research was to develop a custom tool for the robot to operate 2-φ 5 H7 depth 7in an automated way. The experience gained with the PCL extruder Wire hole (P.C.D.60) (same asin ODICO led us to design an early version of PLA throughout the workshop opposite side) extruder to start experimenting with the variables involved in the robotic 23 fabrication.

170° 14 The end effector was developed for a Nachi MZ07 robotic arm and allowed to experiment with plastic extrusion within the studio environment. φ 23 Extrusion speed, waiting time and robot speed are variables that have 73 (Diameter (P.C.D.60) ofbeen wire hole) experienced and 83tested.

7kg Payload 30kg Weigth

4-M5 depth 7 (P.C.D.60)

723mm Max Reach

e nce am re Fr er fe int dius ra 150 R

170°

dB

IP67

23

Phase 1 1249 1602

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73

340 440

-136°

< Axis 3 <

-135°

< Axis 2 <

+-190°

Axis 4

+-360°

Axis 6

+80°

+270°

73 83

70.2 16.6 Nm 110.7

Axis 1

14

φ 23 (Diameter (P.C.D.60) of wire hole)

221.4

+-170°

Wire hole (same as opposite side)

Load Torque

φ 72 h7

Dust / Drip

2-φ 5 H7 depth 7 (P.C.D.60)

φ 45 h7

170°

+-120°

Axis 5

50 45

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Chapter 5: Robotic Fabrication 95.5±0.1 190.5

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190mm H 70mm

2d

70£

W

4-M5 depth 7 (P.C.D.60)

217mm L

2-φ 5 H7 depth 7 (P.C.D.60)

Approximate Dimensions

Total Man / Day to Complete

Estimated Cost

23

φ 45 h7

φ 72 h7

Wire hole (same as opposite side)

14

φ 23 (Diameter (P.C.D.60) of wire hole)

73

Nachi MZ07

83

3D printed core

Ventilator 95.5±0.1 4-φ 11

198

Custom assembly ring

190.5 Cooling 160Unit

189.5

94

Heater relay 160

95.5 ±0.1

Extruding Unit

Extrusion speed potentiometer

Arduino UNO shield

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Endeffector Design

Step_1

Step_2

Step_3

Step_4

Cutting

Step_5

Step_6

Robotic sequence: extrusion of discrete members of raw material.

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Robot Speed

5 s/f

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Extrusion Speed

5 cm/s

Chapter 5: Robotic Fabrication

Waiting Time (between each coordinate)

10s

Global sequence speed

Global Reliability

Model stiffness

Endeffector reliability

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Endeffector Design

ENDEFFECTOR 2.0 Main weakness of the former experiments was the connectivity between members. The imperfect nature of the plastic rods influenced consistently the correct positioning in space causing main deviations from the digital model. The assembly was also difficult because the reduction of material due to the melting in the ends of each rod. The propagation of deviations throughout the entire model caused difficulties in scaling up the fabrication to more then few members.

The use of a second material, (PCL) has been considered as a gluing system between members without having to locally melt the rods themselves.

This required the development of a new end effector which embedded a cutting device and a second extruder for the deposition of the gluing material (PCL) with the aim of a full automation of the entire fabrication process.

Further decision has been to move all the electronics offboard in a dedicated control panel which allowed more manageability and more direct control of the extrusion speed controller.

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Endeffector Design

237mm

Ventilator

H

3d

170mm

100ÂŁ

W 172mm

Cutting Unit L

Approximate Dimensions

Total Man / Day to Complete

Estimated Cost

Servo Motor

Ventilator

Nachi MZ07

Custom Assembly Ring

Stepper Motor NEMA 17

PCL Extruder

PLA Extruder

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Stepper Motor NEMA 17

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Heating Phase

Heating Phase

180s

5 s/f

Robot Speed

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PLA Extrusion + PCL Feed

Orienting

5s

5 cm/s

Extrusion Speed

Chapter 5: Robotic Fabrication

PCL Feed

5s

Cutting Segment

PLA Feed + Movement

5s

Detaching Segment

Cooling Phase

15s

Detaching Segment

5s

20s

Waiting Time (between each coordinate)

Global sequence speed

Global Reliability

Model stiffness

Endeffector reliability

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Endeffector Design

ENDEFFECTOR 3.0 The experiments conducted with the former end effector setup underlined a the necessity to simplify the entire sequence. The end effector implied the execution of complicated operation which made each step of the entire sequence not reliable and not solid enough for an automated procedure.

Further step in the development was to re-evaluate the entire sequence fragmenting the single operations of the end effector in off board and on board steps.

The extrusion and cutting phase was moved off board in a dedicated station with the aim of deploying discrete elements with custom lengths in a more consistent way. The third generation of end effector was a gripper tool utilised to collect and locate in space the discrete elements previously prepared by the cutting station. The assembly of multiple elements happened with the help of manual melting with a soldering tool.

A second robot was considered to work in a collaborative environment with the assembly one. The experiments carried on clarified the feasibility of the process with more precision and consistency of the formers. The proxy material utilised (PLA), guaranteed an agile experimentation with a Nachi MZ07 robotic arm, due to the lightweight material properties. The robotic arm precision and capability to keep location in space for unlimited time has been utilised within the process to negotiate with the phase changing properties of the material in use.

The fabrication sequence explored, showed the possibility to scale up the process to bigger machines and move forward to a stiffer material: Mild steel. 3mm rods were utilised for further manual experiments using spot welding as technique to assemble the discrete elements, resulting in more stiff and rigid models.

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Endeffector Design

_05.2

Endeffector Design

73mm H 100mm

1d

35ÂŁ

W 156mm L

Servo Motor

Approximate Dimensions

Total Man / Day to Complete

Estimated Cost

Ultrasonic Sensor HC-SR04

Nachi MZ07 Gripping Unit

Stabilisers Extruded Aluminium Profile 20x20

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Servo Motor

250mm H 100mm

2d

70ÂŁ

W 186mm L Total Man / Day to Complete

Approximate Dimensions

Ultrasonic Sensor HC-SR04

Cutting Unit

Extraction

3 Filament holder

Cutting Area

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Estimated Cost

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Material Feeding

2

1

Stepper Motor NEMA 17

Cutting Area

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_05.3

Fabrication Sequence

The fabrication sequence is divided into four main moments: Bespoke cut of discrete elements: a cutting station has been designed in order to deploy segment with custom lengths according to the structural need. The segments are cut and kept in place within the station to allow the robotic arm to record the position and automate the gripping process throughout the sequence. A proximity sensor has been installed to actuate the cutting process according to the position of the robotic arm and give indipendency from external actors.

Gripping: a custom end-effector has been developed with a linear gripping function in order to pick the discrete elements. The actuation of the tool happens through the digital output of the robot; the connection endeffector / digital output allows to have the gripping process indipendent from an external controller or actuator, increasing the consistency and reliability of the whole process.

Robotic arm setup with off board controller.

Spatial positioning: the segments are gripped, positioned and oriented in place according to the network configuration.

Assembly: the segments are assembled togheter through the collaboration of a second robotic arm with a soldering tool. The second robot is actuated in sequence after the positioning is completed.

Main objective is to keep the sequence as consistent and solid as possible as well as simple, with the aim of a full automated assembly process. The sequence developed is mostly scale indipendent and could be migrated to bigger robotic arms for a 1:1 application. Spot welding has been thought as process of assembly with the use of metal rods instead of plastic.

The use of heavier rods requires a more reliable endeffector with higher torque force motor, more solid materials and a double gripping unit to avoid the rod instability problem during the robotic movement.

A fourth generation endeffector was designed to counter these issues and propose the further step of the development.

Cutting station to deploy segments with custom length.

Linear gripper to position and orient the discrete elements.

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Endeffector Design

190mm H

Extruded Aluminium Profile 20x20

70mm

2d

70ÂŁ

W 217mm

Ultrasonic Sensor HC-SR04

L Approximate Dimensions

Total Man / Day to Complete

Estimated Cost

Servo Motor

Stabilisers

Nachi MZ07

Custom Assembly Ring

Gripping Unit

Adaptable Joint

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Endeffector Design

Welded models using 3mm steel rods.

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_04.3

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Algorithmic Geometric Construction

Phase 1

Chapter 4: Computational Research

_06

Chapter 6: Architectural Application _06.1 Introduction _06.2 High Performance Cities _06.3 References

_06.1

Introduction

INTRO More then half of the world’s population now lives in cities. The global housing crisis, new advances in digital sharing, the proliferation of slums and the increasing need for social housing are reshaping our idea of home and the architecture of our cities1.

Uncertainties dominate the future of our cities and our lifes, through the gloabel economic crisis and growing will of independency and intolerance.

International hubs like London, New York, Tokyo and Paris are facing a revolution in terms of their urban landscape morphology due to an increasing lack of architectural unity and coherence. What was considered science fiction is becoming reality: in the best case cities are envisioned as amusement parks (Image 40), filled with roller coasters and new playful landmarks, in the worse they become victims of genericness, repetition and homogeneity (Image 41). Good design never mattered more then today, to reverse a global condition of uncontrolled and unregulated growth which leads to urban sprawl and chaos.

Image 40. Futuristic amusement park in London.

The contemporary age offers at the same time multiple opportunities thanks to the steep development of technology and robotic intelligence which we believe will be main driver of a new era of prosperity if coupled with an innovative charge in architectural terms. Networks of communication (Image 42), multi-layered differentiation and organization, and multi disciplinarity are core aspects to consider and bring on board for a novel sophisticated approach to architecture and the built environment.

The suggested approach promotes an adaptive and rapid process to deploy architectural solutions through collaboration with robotic technology.

Flexibility and adaptivity are achieved through the use of discrete elements as building components within a geometric and structural efficient framework. The architecture proposed looks for flexibility, structural efficiency, material and cost reduction and expressive freedom as reaction to the increasing complexity of the contemporary way of living.

Image 41. Kangbashi New Area in Inner Mongolia, the Meixi Lake area near Changsha, and Tianjin’s Yujiapu Financial District.

1 Patrik Schumacher, Gearing up to Impact the Global Built Environment , Published in: AD Parametricism 2.0 – Rethinking Architecture’s Agenda for the 21st Century Editor: H. Castle, Guest-edited by Patrik Schumacher, AD Profile #240, March/April 2016.

Image 42. Communication system in a neural networks.

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TOKYO1 2

Population 13. 617, 445 mi Population (Metro Area) 37. 800, 000 mi

Density 6,224.66 in/km2 Density (Metro Area) 2,662 in/km2

Image 44. . Population of Tokyo estimates 1920-2016

Child Population (0 - 14) 1.477 mi

11,4%

Working Age Population (15 - 64) 8.85 mi

68,2%

Aged Population (65 and over) 2.642 mi

20,4% Image 45. Population age.

Labor Population 6.387 mi

46%

1 Population of Tokyo - Tokyo Metropolitan Government [WWW Document], n.d. URL http://www.metro.tokyo.jp/ENGLISH/ABOUT/HISTORY/history03.

2 Statistics Japanâ&#x20AC;Ż: Prefecture Comparisons [WWW Document], n.d. . Statistics Japanâ&#x20AC;Ż: Prefecture Comparisons. URL http://stats-japan.com/.

Image 46. Daytime - night time population.

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1 2

Daytime

Night time

120%

15.576 mi

100%

20%

13.159 mi

44.6%

61.1%

Home ownership (Tokyo)

Home ownership (national)

42.5%

29.7%

People living alone (Tokyo)

Average salary 250,000 yen

People living alone (national)

House Rent 76,648 yen

Average residential land price 309,700 yen

1 Population of Tokyo - Tokyo Metropolitan Government [WWW Document], n.d. URL http://www.metro.tokyo.jp/ENGLISH/ABOUT/HISTORY/history03.

2 Statistics Japanâ&#x20AC;Ż: Prefecture Comparisons [WWW Document], n.d. . Statistics Japanâ&#x20AC;Ż: Prefecture Comparisons. URL http://stats-japan.com/.

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NEW YORK1 2 3 4 5

Population 8.550, 405 mi

Density 10,831.1 in/km2

Population (Metro Area) 20. 182, 305 mi

Child Population (0 - 14) 1.516, 961 mi

19%

Working Age Population (15 - 64) 5.87 mi

68%

Aged Population (65 and over) 1.098 mi

20,4%

Image 49. Average household income.

Unemployment Rate 358,408

6.1%

1 https://www.timeout.com/newyork/blog/map-of-average-rent-by-nycneighborhood-is-as-depressing-as-youd-expect-082115.

2 http://www.zillow.com/new-york-ny/home-values/ 3 http://cityroom.blogs.nytimes.com/2013/06/03/commuters-nearly-doublemanhattans-daytime-population-census-says/ 4 https://www.6sqft.com/what-nycs-population-looks-like-day-vs-night/ 5 http://www.baruch.cuny.edu/nycdata/population-geography/pop-demography.htm Image 50. Daytime - night time population.

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1 2 3 4 5

Daytime

Night time

100%

3.1 mi

90%

90%

1.6 mi

25%

63%

Home ownership (NYC)

Home ownership (national)

48%

354,336 in

People living alone (NYC) Average salary 48,631 dollars

House Rent 3500 dollars

Average residential land price 586,900 dollars

1 https://www.timeout.com/newyork/blog/map-of-average-rent-by-nycneighborhood-is-as-depressing-as-youd-expect-082115.

2 http://www.zillow.com/new-york-ny/home-values/ 3 http://cityroom.blogs.nytimes.com/2013/06/03/commuters-nearly-doublemanhattans-daytime-population-census-says/ 4 https://www.6sqft.com/what-nycs-population-looks-like-day-vs-night/ 5 http://www.baruch.cuny.edu/nycdata/population-geography/pop-demography.htm

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LONDON1234

Population 8.673, 713 mi

Density 6,390 in/km2

Population (Metro Area) 13. 879, 757 mi

Child Population (0 - 14) 1.561, 268 mi

18% Image 53. People living alone by age and sex.

Working Age Population (15 - 64) 6.33 mi

73%

Aged Population (65 and over) 780,634 mi

9%

Image 54. London house prices.

Unemployment Rate 354,581

5.6%

1 http://www.citylab.com/housing/2016/02/londons-renters-now-outnumberhomeowners/470946/ 2 http://www.telegraph.co.uk/news/politics/10863343/Huge-rise-in-the-singletonlifestyle.html 3 http://www.londonspovertyprofile.org.uk/indicators/topics/ 4 https://www.quora.com/Which-city-in-the-world-has-the-biggest-differencebetween-the-day-and-night-population Image 55. Population of London estimates 1801-2011.

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1234

Daytime

Night time

100%

390,000

30000%

30000%

11,700

50%

64%

Home ownership (NYC)

Home ownership (national)

35%

3,035,799 in

People living alone (NYC) Average salary 34,000 GBP

House Rent 743 GBP per room

Average residential land price 643,843 GBP

1 http://www.citylab.com/housing/2016/02/londons-renters-now-outnumberhomeowners/470946/ 2 http://www.telegraph.co.uk/news/politics/10863343/Huge-rise-in-the-singletonlifestyle.html 3 http://www.londonspovertyprofile.org.uk/indicators/topics/ 4 https://www.quora.com/Which-city-in-the-world-has-the-biggest-differencebetween-the-day-and-night-population

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High Performance Cities

ANALYSIS A deep analysis of three urban hubs has been conducted to understand the major trends of their populations and how these live the urban environment.

Tokyo, New York and London were considered as three perfect examples of high performance cities, characterized not only by high population and density but also by an high speed metabolism which transformed the way people live in the last few decades.

The number of people living in the cities is steeply increasing because of their attractive power in terms of job opportunities and multidisciplinary possibilities thanks to their powerful network of communication and worldwide investors. This transformation phenomenon into global hubs, multi ethnic and differentiated, fueled the adaptation of the morphology of these cities into more responsive and dynamic entities to counter the high demand of their population and the increased performances.

Due to boomed financial activities and real estate speculations prices for renting and mortgages have been increased. This has changed the way that people live, their everyday activities and private life in addition to its direct impact on the housing market. The former data clearly identify a global tendency to live outside of the city centers in small spaces for one person only, and utilise the efficient public transportation network to reach the work place.

The rooted idea of â&#x20AC;&#x153;homeâ&#x20AC;? gradually changed; for most of the people home doesnâ&#x20AC;&#x2122;t represent anymore a private space to spend their free time and disconnect from the rest of world, but a place to be even more connected then during the daily routine, an adaptable space able to serve more functions, privates and socials. Shared houses and accommodations are becoming the norm for young generations of students and workers which cannot afford bigger spaces in the city centers.

The shared concept of living redefined the way public and private spaces are distributed within the buildings and the idea of privacy itself.

Adaptability

Fast Deployability

Connectivity

Cost Reduction

The entire housing system is changing and has to be rethought and redesigned to tailor the needs of the contemporary way of living.

Adaptability, fast deployability, connectivity, flexibility and cost reduction are key concepts to evaluate and consider to design the house of the present and future.

Flexibility

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Location: Shimbashi, Tokyo, Japan.

Architect: Kisho Kurokawa Building Description: The Nakagin Capsule Tower is part of is a mixed-use residential and office tower completed in 1972. As part of Japans post war architecture, one of the last remaining examples of Japanese Metabolism and the worlds first arranged capsule building. Used was the building for permanent residents and practical purposes. Nowadays only 30 of 140 capsules are actually inhabited, the remaining capsules are used as storage, offices or are completely abondend and left to self-destruct.

The tower has 13 floors and a total area count of 3,091.23 square meters.

Each micro apartment has an area count of 8,74 square meters, and the measurements for each capsule are 2.3 m x 3.8 m x 2.1 m. One capsule can accomodate 1-2 people. To achieve bigger spaces multiple capsules can be connected. The capsules where designed and ulilized before they were shipped to the site and mounted onto the concrete circulation shaft.

They are ment to be replaceable, despite till today none of the capsules has been replaced.1

Nakagin Capsule Tower, inside capsule.

1 https://en.wikipedia.org/wiki/Nakagin_Capsule_Tower

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References

Location: Anywhere Architect: Tomas Zacek, Sona Pohlova, Matej Pospisil Building Description: The Ecocapsule is a apparently self-sustaining capsule, which can be placed anywhere there is solid ground. The architects and designers describe the Ecocapsule as a â&#x20AC;&#x153;mobile homeâ&#x20AC;?1, which is able to source energy through wind and solar power and collect rain water. Geographic and local condition dependencies impact the duration the capsule can be used without connecting it to the grid. A standard value of one year can be expected.

The mobile home is able to accomodate 1-2 people and has am usable area count of 6,3 square meters. Basic specifications of the capsuled measure 4,45 m x 2,25 m x 2,55 m.

Lightweight materials are used to construct the ecocapsule: Insulated fibreglass as the outer shell, an aluminum framework with a honeycomb and furnier wood interiour. Kitchen, washing cabinet and lighting is standard utilisation in the capsule. The capsules electric system is controlled with a tablet app which is fed by the smart home system and sensors.

Ecocapsules with folded out wind wheel.

1 https://www.ecocapsule.sk/#product-info

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Location: United States of America, New York, New York City Architect: nArchitects Building Description: As a sensible reaction to rapid growth of New York Cities small household population nArchitects developed a “new standard for micro-living”1. High felxibility and adaptability thanks to modular construction atracted a lot of attention, for especially in NYC where the highest density of people world wide is measured. nArchitects micro-unit project is the first in New York City.

The micro apartment unit which is called Carmel Place provides space for “55 loft-like”1 individual living areas. Four different types of apartments are available, with area counts ranging from 7 to 9,7 square meters. Small footprint but a desire for high living standard, a certain spaciousness and comfort requiers extra efficiency and intelligent design decisions for interiour and program.

Each of the apartment modules were locally prefabricated, foundation and ground floor were built on site. In total 65 self-supporting steel framed modules where stacked on site, 55 as ibhabitable spaces, 10 as core and circulation.

Ecocapsules with folded out wind wheel.

1 https://www.ecocapsule.sk/#product-info

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References

Nowadays, the way that people seek for a living space has changed. People are expecting less private space and more communal space in order to communicate and integrate with other key factors of urban life. This opens up a new concept, “Co-Living”. In every other industry there is an ownership model and service model. Property market has co-living to serve those two economic models.

The Y generation is choosing to avoid ownership as use of Uber, mobile phone contracts, rental bike services demonstrates. This type of living is supporting multi generational creative community and reviving the old accustomedness of bonded neighbouring as the previous generation had.

In space planning, this leads to designing the private spaces served by communal spaces which are enhancing inter disciplinary exchange of knowledge and make permanent social connections. The “living room”, concept is eliminated from the existing traditional housing diagrams and integrated matrix of the building.

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