Data-Driven Hybrid Structures

DATA-DRIVEN HYBRID STRUCTURES Informed Multi-Materiality Fabrication To Achieve Emerging Optimized Architectural Spaces Master Thesis by: Mohamed S. Moharram Matriculation Nr. : 4065391

DIA Master Program SS 17/18 Studio Design to Robotic Production & Operation First Supervisor: Prof. Henriette H. Bier Second Supervisor: Sina Mostafavi

I. Acknowledgement The studio research is considered as a collaboration between DIA Robotic lab and Robotic Building Research group at TU Delft and was conducted for the fulfilment of the Master Degree of Architecture in Dessau International Architecture Program, Faculty of Architecture, Hochshule Anhalt. I thank all of my supervisors, colleagues and technical stuff from DIA Robotic lab and TU Delft Robotic Building who provided insight and expertise that greatly assisted the research and the physical work.

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To Everyone Who Knows Me ......

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"The Age of Mankind is over. A new world has begun! The rule of Robots!"

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Karel Čapek

II. Preface Design-to-Robotic-Production and -Operation (D2RP&O) focuses on the integration of advanced computational design with robotic techniques in order to produce performance driven architectural formations. This implies that design is directly linked to building production and operation. The studio encourages students to question conventional design processes in order to creatively challenge the interplay between contemporary culture, science, and technology, and their relation to architecture. D2RP links design to materialisation by integrating all functionalities (from structural strength, to thermal insulation and climate control) in the design of building components, while D2RO integrates robotic devices into building components in order to facilitate spatial and climatic reconfiguration. Together they establish the framework for robotic production and operation at building scale. The main consideration is that in architecture and building construction the factory of the future will employ building materials and components that can be robotically processed and assembled. Thus D2RP&O processes incorporate material properties in design, control all aspects of the processes numerically, and utilise parametric design principles that can be linked to the robotic production.

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III. Keywords Mass Customization, Digital Architecture, Data-Driven Design, Point Cloud, MultiMateriality, Existing Structures, 3D Printing, Caly Printing, Robotic Fabrication, Hybrid Materialization, Prototyping

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IV. Abstract Mass customization and Digital Fabrication have been introduced, allowing digitally driven-technology and computational systems to be widely integrated into many industrial manufacturing processes. This implicates the applications of robotic production towards controlled customized production and new design in building technology. Nowadays, most of the new technologies and new materials are applied to the new constructions while the major structure of our cities is old structures which were built before 2000. For example, the EU report for construction output shows a big reduction of the newly built structure especially after the economic crisis in 2008. On the other hand, A the building lifecycle is getting longer due to the big improvement of construction technology and materials.

So What If, we break the linear chain of Building lifecycle, we created a feedback loop between data from existing structure to new adaptive architectural upgrade, we can upgrade our homes like a computer hardware by adding more features and spaces. What would be the shape of architecture emanates from existing Structures?.

The main question to be explored is, in the age of Digital Manufacturing how could robotic digital fabrication improve the characteristics of construction materials to provide a new use of existing spaces which to be optimized environmentally and structurally.

Since this topic is one of the recent emergent science in the field of architecture dealing with digital fabrication. Also, many researchers have approached have approached this topic from many different perspectives. Though studying and analyzing multiple case studies would be very useful for understanding the potentials of the technology and how to apply it in Architecture. The work presents a customized production framework which is informed through digital optimization methodology that consists of virtual data cloud system. In this system, every part of data gives a different type of information and every part has its characteristics of physical or nonphysical properties.

The framework that is presented here is examined through using optimization method to produce 1:1 robotically produced prototype of a multi-layering hybrid component.

So, the digital model is divided into different layers that will be produced using the robot. A closed feedback loop is produced so we make use of all the available data.

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V. Table of Contents I. II. III. IV. V. VI. VII.

Acknoledgement Preface Keywords Abstract Table of Contents List of Tables List of Figures

2 3 6 7 9 10 11

01. Chapter 1: Literature Review Design to Production

15

02. Chapter 2: Hypothesis & Research Question Optimization of Architectural Spaces

23

03. Chapter 3: Methodology Data-Driven Design

41

04. Chapter 4: Procedure & Applications Hybrid Materialization

81

05. Chapter 5: Conclusion rOBOTICW Prototyping

113

1.1 Introduction 1.2 Digital Techniques 1.3 Case Studies

2.1 Robotic Fabrication 2.2 Problem Statement 2.3 Parasitic Architecture 2.4 Research Question 2.5 Context 2.6 Open Source Manufacturing 2.7 Framework

3.1 Digital Design 3.2 Optimization of Architecture Spaces 3.3 Macro Scale 3.4 Building Design 3.5 Meso Scale 3.6 Voxelization

4.1 Digital- Driven Materilaization 4.2 Meso Multi-Hybridity 4.3 Micro Scale 4.4 Voxel Materialization

5.1 Robotic Prototyping 5.2 Conclusion 5.3 Further Investigation

VIII. Bibliography

122

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VI. List of Tables Table 2.1 Comparing selected Urban fabrics Beijing, New York, Stuttgart Table 2.2 Case studies for different parasitic architecture Approaches Table 2.3 New Program of Micro Manufacturing Space

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27 28 33

VI. List of Figures Chapter 1 Figure 1.1 DIA Design to production studio ss16/17 milling prototype for hybrid topologies project Figure 1.2 Mass Production vs Mass customization relation to users Figure 1.3 Industrial Evolution through 4.0 cyber technologies Figure 1.4 Different types of computer driven fabrication technologies Figure 1.5 Different types of computer driven fabrication technologies Figure 1.6 TEX-FAB winner Plastic Stereotomy Chapter 2 Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6 Figure 2.7 ِ Figure 2.8 Figure 2.9 Figure 2.10 Figure 2.11 Figure 2.12 Figure 2.13 Figure 2.14 Figure 2.15 Figure 2.16 Figure 2.17 Figure 2.18 Figure 2.19 Figure 2.20 Chapter 3 Figure 3.1 Figure 3.2 Figure 3.3

Design to robotic production and operation loop EU report for construction output Germany report ofConstruction Output Standard building and construction Life cycle process Charts addressing the percentages of Urban fabrics operation Thesis Research and Design Outline Average Annual Growth Population World Wide Berlin Map Highliting the ring railway areas Warehouse and Factories located on Berlin ring Railway Site Images Site Model Intervention on old building growing a new typology New Typologies of Manufacturing Spaces Existing Standard Manufacturing space vs Smart Cyber Manufacturing Space Current Manufacturing Space Typologies Reduction of steps from current Manufacturing to smart ones Smart Manufacturing unit Variation of arrangements of Manufacturing space Cyber Physical Manufacturing Space Thesis Framework

Octopus Screen showing various iterations of generating geometries based on the same objectives Design different data cateogeries Data objectives for Architecturak Design

17 18 19 19 20 21

24 26 26 26 27 29 30 30 31 31 31 32 32 33 34 34 35 35 36 38

43 44 44 Page 11

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Figure 3.4 Figure 3.5 Figure 3.6 Figure 3.7 Figure 3.8 Figure 3.9 Figure 3.10 Figure 3.11 Figure 3.12 Figure 3.13 Figure 3.14 Figure 3.15 Figure 3.16 Figure 3.17 Figure 3.18 Figure 3.19 Figure 3.20 Figure 3.21 Figure 3.22 Figure 3.23 Figure 3.24 Figure 3.25 Figure 3.26 Figure 3.27 Figure 3.28 Figure 3.29 Figure 3.30 Figure 3.31 Figure 3.32 Figure 3.33 Figure 3.34 Figure 3.35 Figure 3.36

Data Driven Design Octopus Evaluation screen Octopus 128 Population Different generations of Stacking Digrams Mesh from stacking volumes 24 Selective population of stacking volumes 24 Selective population of stacking volumes in a point cloud 24 Selective population of Volume cloud of points 24 Selective Meshes generated from stacking points cloud Selected iteration for the design Axnometric view showing the design different parts Define a part for Meso Scale Ground Floor Plan Scale 1:750 First Floor Plan Scale 1:750 Second Floor Plan Scale 1:750 Longitudinal Section Scale 1:750 Cross Section Morphology Layers Scale 1:400 24 Cross Sectional Morphologies Scale 1:1000 Interior Rendering 01 Interior Rendering 02 Architectural Factors Meso Design Data Classification Meso D2RP&O Data Factors Programmable Wall transition Meso Framework Meso D2RP&O Data Factors DIA SS16/17 Meso Cloud of Points, Topological Hybridity Minimal Surfaces Voxelization Voxel Cells Types Voxel Matrix Meso Voxelization (Iteration 01) Hybrid Meso Voxelization 02 (Iteration 02) Hybrid Meso Voxelization 02 (Iteration 02)

45 45 46 48 49 51 53 55 57 58 59 59 60 60 60 61 61 62 64 66 68 68 68 69 69 70 70 71 73 75 76 88 79

Chapter 4 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 4.5

Meso Part Design Meso Data generations Meso Cloud Iterations Meso Data Cloud Iterations Meso Cells Cloud Iterations

82 83 84 87 89

Figure 4.6 Figure 4.7 Figure 4.8 Figure 4.9 Figure 4.10 Figure 4.11 Figure 4.12 Figure 4.13 Figure 4.14 Figure 4.15 Figure 4.16 Figure 4.17 Figure 4.18 Figure 4.19 Figure 4.20 Figure 4.21 Figure 4.22 Figure 4.23 Figure 4.24 Figure 4.25 Figure 4.26 Figure 4.27 Figure 4.28 Figure 4.29 Figure 4.30

Meso Voxels Iterations Meso Part 3d Section Meso Multi Hybridity Part 3d Section Meso rendering 01 Meso rendering 02 Multi-Materiality Matrix Additive manufacturing matrix Hybrid Techniques Tex Fab Hybrid Plasticity Volumetric Tessellations Interlocking Geometries Linear Differential Growth Hybrid Techniques Hybrid Techniques Hybrid Interlocking 3d Grid 3d Printed Hybrid Interlocking Grids two Parts 3d Printed Hybrid Interlocking Grids Assembled Voxel Tectonics and Porosity 01 Voxel Tectonics and Porosity 02 Voxel Tectonics and Porosity 03 Voxel Tectonics and Porosity 04 Voxel Tectonics 3d Printing Hybrid Voxel and prorosity 3d printing Production Process of Two Hybrid double curved Surfaces Digital Hybrid Prototype Digital Deformed Hybrid Prototype

91 92 93 94 96 98 99 99 100 100 101 101 101 102 103 103 103 105 106 107 108 108 109 110 111

Chapter 5 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 5.7

Silicon Robotic Printed Prototype Materials for Building the Extruder Extruder Testing and producting first Prototype Extruder Design Extruder robotic Fabrication Milling the double curved surface Material Extrusion on the double Curved Milled Surface

115 117 117 118 118 119 120

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Chapter 1 Literarture Review

DESIGN TO PRODUCTION

Description This chapter represents an introduction to the idea of customization of architecture through digital fabrication and how this concept has been developed through tha last 20 years including some case studies and different fabrication methods.

1. DESIGN TO PRODUCTION 1.1 Introduction Design for manufacturing (DFM) is a design technique of parts that would be manufactured in a simple way of an assortment to constitute the ultimate product after assembly. Minimizing the complexities involved in production process is one of the main concepts for design for manufacturing as well as managing and maintaing the aspect of the production cost. in order to creatively challenge the interplay between contemporary culture and technology, and their relation to design. The design is directly linked to production and operation with the goal to develop physically prototypes and direct feedback processes. This allows to rethink conventional design Since mass customization has been introduced as major potential against the common mass production strategies, digitally driven-technology and computational systems have been widely integrated in many industrial manufacturing processes. This implicates the applications of robotic production towards controlled adaptive and optimized design in building technology Nowadays, a punch of new technologies appears eveyday no matter how would it be used and integrated in different science and arts fiels or even human society integration. This makes the the possibilities wider and larger for everyone. The idea is how you are gonna make use of these technologies into your

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work and engage it in the right manner to achieve new and creative work. Even now the technology becomes smarter an d smarter one can learn form it and it can learn for eveyone.

1.1.1 Principles of Design to production Standardization; using neutral design with different charachteristics can help reduce the time for the process.

Less number of parts; minimizing the number of parts in a product is an effective way to reduce production costs without sacrificing quality or performance.

Modular machinig and assembley; design to minimize manual interaction during production and assembling.

Create Modular Assemblies; Using noncustomized modular assemblies in design will allow or product modification as needed without losing the overall functionality on the back end.

Define Acceptable Surface Finishes; Designing acceptable finishes for function and not for aesthetics.

Maintain the number of Operations; Making process straight forward and cost effective innovations are the key to DFM

Chater 1: Design to Production

Figure 1.1 DIA Design to production studio ss16/17 milling prototype for hybrid topologies project

1.1.2 Design for Industrial Robots Digitally driven- technology and computational systems have been widely integrated in many industrial manufacturing processes. This implicates the applications of robotic production towards controlled adaptive and optimized design in building technology.

Allowing for new achievements in building industry. This is how industrial programable machines are changing the future and allowing for bigger possibilities. No one could have imagined we can produce Architecture in a factory 50 years ago. Now it is possible because of this technology.

Starting from DIA - Dessau institute of Architecture- robotic lab milling prototyping (Figure. 1) which is based on designing for production usages in architecture for creating EPS mold for casting comples shpapes of concrete.

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1.2 Digital Technologies 1.2.1 Mass Customization vs. Mass Production Through the last two decades, Mass customization has been introduced as major potential technology against the common mass production technologies due to the fast expanding market of human needs in everything. In every product that was produced using mass customization strategy, different needs and desires of different users, clients or buyers are considered. On the opposite, mass production strategies has a fixed production line with a fixed product which every product has the same specifications and the shape of the previous produced one. figure 1.2 Mass Production vs Mass customization relation to users

Before [Mass Production]

After [Mass Customization]

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The concept of mass customization was first produced by Stan Davis in Future Perfect. Mass Customization is defined as "producing goods and services to meet individual customer's needs with near mass production efficiency"1. Nowadays, Most of the production Lines in any factory have been changed through time due to the emergent technologies towards more intelligent Techniques. In modern factory, flexible manufacturing technologies facilitate efficient transition from ideas to finished products. In the next years it is expected to reach every business model and to be developed through time.

Chater 1: Design to Production

1.2.2 Industry 4.0 Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies.

Selforganized logistics machines which can

predict failures and trigger maintenance processes autonomously or which react to unexpected changes in production are considered examples for Industry 4.0.

figure 1.3 Industrial Evolution through 4.0 cyber technologies.

Mechanization, Water Power, Steam

Mass Production, Assembley Line, Electricity

Computer and Automation

1.2.3 Digital Manufacturing Smart is the new era of technologies where data is transmitted between every user and been analyzed for better development of the humanity. Industry is changing towards users with smaller machines and more potentials.

Cyber Physical System

Technologies like 3d printing, laser cutting and CNC machines are changing the shape of evey industry spreading from products spreading from consumer goods, construction to basic services. figure 1.4 Different types of computer driven fabrication technologies

MATERIAL JETTING

LASER CUTTING

CNC MILLING

3D PRINTING

Digital Manufacturing

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1.3 Case Studies

Figure 1.5 Different types of computer driven fabrication technologies.

Resource: https:// www.branch. technology/ projects-1/2017/6/9/ shop

1.3.1 Case Study No. 1 2016 Design Miami Visionary Award, Shop Pavillion, Branch Technology The team tried to develop a design to draw inspiration from Miami's celebrated spirit of play and dynamic beach morphology representing the city's emergent function for creativity and Technology.

‘SHoP represents exactly what the panerai design miami/ visionary award is meant to recognize: innovation, inspiration and an outstanding point-of-view,’ says rodman primack, chief creative officer, design miami/. ‘for the first time, we will be installing the commission long-term in the miami design district and i cannot think of a better practice to conceive this installation. we are thrilled with the pavilion design and delighted to honor shop for the 12th edition of design miami.’

The Pavilion was divided into small parts which later are developed applying large-scale 3D printing by industrial robot and then Assembled at the Site.

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Chater 1: Design to Production

1.3.2 Case Study No. 2 Tex- Fab 2014 1st Award, Plastic Stereotomy, Justin Diles, Ohio State University The case study chosen here is an exploratory regarding the expressing the idea of Hybridity by it is also descriptive regarding the exploration of the computational methods to create such combination of two materials in a useful way.

Figure 1.6 TEX-FAB winner Plastic Stereotomy Resource https://www.archdaily. com/564444/ justin-diles-winstex-fab-plasticitycompetition-withplastic-stereotomy

Plastic stereotomy is exploring combining two different materials to create a self-standing structure using Laminar FRP and Solid Foam. The digital way of producing this kind of hybridity using volumetric tessellations and computational methods is to be explored and analyzed in order to create a new way for producing different alternatives for the combination of multi materials regarding the structural, physical, environmental and economic characteristics of 3d printed Architectural elements

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Chapter 2 Hypothesis & Research Question

Optimization of Architectural Spaces

Description In this chapetr a lot of research queries has been addressed starting from the problem statement to the the final reasearch question through a lot of what if questions and different proposals for the solution and applications.

2. Optimiziation of Architectural Spaces 2.1 Robotic Fabrication In Architecture Design for Robotic productions focuses on integrating materialization to design.

This allows the engagement of factors and parameters to control the design and the production process by implementing multi-performative strategies.

The programmable robots are a very effective tool in this process as it can be used as a multi-fabrication tool. Various end-effectors can be applied easily and the machine is easy to program and to be used in creative ways.

analysis, environmental factors, and economic factors have been enhanced and implied in the design.

Design to robotic production and operation (D2RP&O) concept has been applied to architecture in the last 20 years trying to use the potentials of the robots to enhance the quality of the architecture and the variation of design.

Architectural factors like structural

The production and real-time operation are considered through Virtual modeling and simulation interface of physically built space to establish a feedback loop for an unprecedented design to production and operation.

Figure 2.1

D2RP&O establish a framework allowing successful implementation of robotic production and operation at building scale. The main consideration is that in architecture and building construction the factory of the future employs building materials and components that can be robotically processed and assembled.

CONSTRUCTION

Design to robotic production and operation loop.

BUILDING

DESIGN TO ROBOTIC PRODUCTION AND OPERATION Feedback

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Reusing Renovation Extension Demolishing

?

Chapter 2 : Optimization of Architectural Spaces

2.1.1 Digital Prototyping of Architecture Actual prototyping is one of the main trends regarding creating a feedback loop between production and design which informs the way of designing different products. Nowadays, the computer-aided design allows us to link the design with the manufacturing process which provides a way to control the machine work. 3D printing and CNC Machining are the most trending fashion of digital fabrication where different shapes and products are manufactured according to the different digital designs.

In regards to that, Mass customization and Digital Fabrication have been introduced, allowing digitally driventechnology and computational systems to be widely integrated into many industrial manufacturing processes.

This implicates the applications of robotic production towards controlled customized production and new design in building technology.

2.2 Problem Statement 2.2.1 Urban Development Through the past years, urban development has been one of the major areas that involved the latest digital technologies, especially in construction Sectors. But there is always this discrepancy between kind of technologies and new materials used and the existing urban environment.

Nowadays, most of the new technologies and new materials are applied to the new constructions while the major structure of our cities is old structures which were built before 2000.

So What will happen If We break the linear chain of Building lifecycle? We created a feedback loop between data from existing structure to new adaptive architectural upgrade. We can upgrade our homes like a computer hardware by adding more features and spaces.

What would be the shape of architecture emanates from existing Structures?

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figure 2.2 EU report for construction output

figure 2.3 Germany report ofConstruction Output Source: Statistisches Bundesamt (Destatis).

2.2.2 Statistics For example, the EU report (Figure 2.2) for construction output shows a big reduction of the newly built structure especially after the economic crisis in 2008. On the other hand, the building lifecycle is getting longer due to the big improvement of construction technology and materials.

In Germany for the last 25 years, not so many new constructions have been built since 1993. It is now more about renovating an old building or abandon structures and make a new one. However, Germany is one of the best in developing technologies, these new technologies are still not applied to new architectural designs or constructions.

2.2.3 Building lifecycle Stemming from today's building lifecycle we found that many of the processes the architects are not involved so how can the architect create a feedback loop form building a new structure, operating or even destroying a one.

Architects Are not Involoved !!!!

figure 2.4 Standard building and construction Life cycle process

Planning & Design

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Construction & Finishing

Operation & Maintainence

Removing & Demolition

Chapter 2 : Optimization of Architectural Spaces

2.2.4 Urban Plots Analysis Selecting three random areas for 3 cities in different contexts and from different cultures and analysing the urban fabric of these areas, it shows that the new

construction average is 8% of the urban fabric which leave around 87% excluding the free parcels from that - not developed to equal the future user and technology use.

Table 2.1 Comparing selected Urban fabrics for 3 cities; Beijing, New York, Stuttgart

Beijing, China Void Approx. 30 %, Built Approx. 70% Operation Approx. 85 % Construction Approx. 11% Free Parcels Apprx. 4 %

New york, USA Void Approx. 50 %, Built Approx. 50% Operatin Approx. 92 % Construction Approx. 2% Free Parcels Apprx. 6 %

Stuttgart, Germany Void Approx. 65 %, Built Approx. 35% Operation Approx. 86 % Construction Approx. 10% Free Parcels Apprx. 4 %

figure 2.5 Charts addressing the percentages of Urban fabrics operation

Operated Buildings 87 % New Constructions 8% Free Parcels or Demolished Buildings 5%

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2.3 Parasitic Architecture Table 2.2 Case studies for different parasitic architecture Approaches

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Parasite Prefab / Lara Calder Architects Prefab parasite project is meant to populate the unused spaces found in urban landscapes. Fittingly, the parasite will cling to old facades and bridges.

Lebbeus Woods, Sarajevo, Radical Reconstruction, 1997

ZA Architects, Hotel Heart of the District is an Innovative Hotel Lobby that Hangs Like a Parasite Between Existing Buildings.

Parasite Illustration - DOKC LAB Ercolani Bros. Research Department, DoKC lab presenting illustrations of parasite architectural vision on an existing city structures.

Chapter 2 : Optimization of Architectural Spaces

2.4 Research Question 2.4.1 What iff What if, We break the linear chain of Building lifecycle, We created a feedback loop between data from existing structure to new adaptive architectural upgrade, We can upgrade our homes like a computer hardware by adding more features and spaces. So, What would be the shape of architecture emanates from existing Structures?

The main question to be explored is, In the age of Digital Manufacturing how could robotic digital fabrication improve the characteristics of materials

Architectural Context Functional Space Design

to provide a new use of existing spaces which are optimized environmentally and structurally?

2.4.2 Thesis Outline The research is divided into 2 parts; the first one is about the architectural context and how to enhance the architecture space design, the Second one is concerned with the robotic fabrication of optimized architectural prototypes. Both are related to each other through the connected factors for optimization and design.

Mult-Disciplinary Approach

Figure 2.6

Integrated Research Robotic Fabrication

Thesis Research and Design Outline

Architectural Problem Urban Context Building Typology Experimenting Materialization Design Factors Materialization Factors Optimization Production Techniques

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2.5 Context 2.5.1 Manufacturing in Germany Germany is one of the biggest countries in the world and world's first economic power. It is considered one of the largest market for Technology and Industry markets. It is also one of the biggest

countries with an old infrastructure and buildings beside the new technological modern ones. It is considered one of the most rapidly growing countries in the world, and when it comes to manufacturing, Germany reigns supreme as the largest supplier.

figure 2.7 ِAverage Annual Growth Population World Wide

figure 2.8 Berlin Map Highliting the ring railway areas

Siemens Stadt Berlin Railway Ring

Source: https://www. berlin.de/stadtplan/ Berlin Hbf Berlin Alexanderplatz

Berlin S-Bahn

BeRlIn

Berlin Tempelhof

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Chapter 2 : Optimization of Architectural Spaces

Berlin is one of the biggest cities in Germany and the biggest population if all German cities, expecting 5 million people to live in the city by 2050. To Provide all of these people with jobs it would be hard. Meanwhile, with the rapid growth of the economy, especially mega centers and large housing developments are mushrooming in the urban area.

Berlin ring railway is considered one of the oldest and biggest networks in EU. A lot of manufacturing centers and warehouse are located around this area.

Due to the large urban spreading of the city, these areas became now part of the city allowing the growth to go beyond its edges. Some of these areas now became abandoned or non-desirable to people from the city.

figure 2.9 Showrooms, Markets and Warehouses

Construction and Cement Industry

Heating and Electricit Plants

Warehouse and Factories located on Berlin ring Railway

Grocery Products factories and Warehouses Car recycling

Used Products Warehouses and Resellers

figure 2.10 Site Images

So the main idea is to how to transform these areas to a new attraction for jobs.

How to give back these areas to the city and the society, how would it look like to integrate the manufacturing spaces and the offices in the city?

2.7.2 Site Location The selected site is located in South Berlin Schoneberg district. It is an old Baustoffe warehouse. It is located on the ring railway of Berlin. figure 2.11 Site Model

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2.5.3 Existing Building Intervention The main idea is how to grow architecture on existing structure to enhance the existing space design of this building and to form new spaces emerged from

that like the way how parasites work to create a new form of living later. So the best way is to transform the existing Building to be used for new programs.

figure 2.12 Intervention on old building growing a new typology

Existing Industrial Districts Abandoned Factories Closed Factories Old Technology Factories

Equal Space of Normal housing Housing Dwellings High Rise Housing Private House

2.6 Open-Source Manufacturing Space 2.6.1 New Typology 'Design the Machine that Makes the Machine' Stemming from that phrase that was said by Elon Musk, the new way of providing new Manufacturing space is to make to more like a smart machine.

New Typology of Manufacturing figure 2.13

spaces is appearing gradually in the term of Incubators or micro Factories that are more towards research and prototyping experimentation for bigger manufacturing plants. How this new typology would be integrated with the city and be its own.

Existing Infrastructure

New Typologies of Manufacturing Spaces

Growing Population

Industrial District of the Future

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New Mixed Typology (Industrial District of the Future) Industrie 4.0 Mass Customization Clean Energy

Chapter 2 : Optimization of Architectural Spaces

Table 2.4

Design & Innovation

Entrance and Reception 1 Co-Working Area 1 Meeting Rooms

Exhibition & Sales

Services

150 m2 * 1= 150 m2 30 m2 * 1 = 30 m2

12 Fabrication Units

30 m2 * 1 = 30 m2 80 m2 * 12= 960 m2

1 Quality Control

30 m2 * 1= 30 m2

Entrance and Reception

30m2 * 1 = 30 m2

1 Seminar Rooms

100 m2 * 1 = 100 m2

1 Exhibition Units

50 m2 * 1= 50 m2

2 Toilets

20 m2 * 2 = 40 m2

1 Break Lounge

60 m2 * 1 = 60 m2

1 Technical Area

50 m2 * 1= 50 m2

1 Warehouse Manufacturing

30 m2 * 1= 30 m2

New Program of Micro Manufacturing Space

Total Program Area = 1560 m2 Maximum 125 Persons / Day

Figure 2.14 Existing Standard Manufacturing space vs Smart Cyber Manufacturing Space

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2.6.2 Current Manufacturing typologies Nowadays there are so many categories of manufacturing spaces due to the old manufacturing process of mass production. Trying to classify them figure 2.15

into 6 typologies of building such as a warehouse, large scale, small scale, telecommunication, exhibition and research and development factories.

01. Warehouse

Collecting and storing material and products

Current Manufacturing Space Typologies

02. Heavy Industrial Building

Depending on Multi- Systems Production Line

03. Telecom/Data Hosting

Recording online data or telecommunications

04. Light Industry

Simple production line for human use products

05. Showroom

Store outlet for factories and announcing new products

06. R&D

Flex buildings for high technology industries, such as electronics

3D Design

Manufacture Files

Material

figure 2.16

3D Design

Reduction of steps from current Manufacturing to smart ones.

User Feedback

CURRENT MANUFACTURING

Material Preparation

SMART MANUFACTURING

User Feedback

Shopping

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Packaging

Production

Material

3D Printing

Chapter 2 : Optimization of Architectural Spaces

2.6.3 Manufacturing Unit So How will the Programmable Manufacture Space Look like?

1. Users are the engine of Customization 2. (re)integrating manufacturing into

the city. 3. Research-Based Tech-Center 4. Creative Augmentation. 5. Human& Computer Interaction. 6. Data Cloud.

Networking (MindSphere).

Figure 2.17 Smart Manufacturing unit

Augmented Control

Manufacture Space

Workstation

Figure 2.18 Variation of arrangements of Manufacturing space

03. Multiple Fabrication Large -Scale Production

03. Clusters

Cooperating Production Line

02. Twin Units

Different Products, One Fabrication System

01. Unit

Stand Alone Production

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Figure 2.19 Cyber Physical Manufacturing Space

2.6.4 Cyber Manufacturing Space A new typology of manufacturing space is provided to host new kind of user-friendly mass customization of different products. This new space is more network-based and based on Industrie 4.0 principles. So how would the architecture that will host these new technologies look like especially when it is now user-driven manufacturing applying cross-relations between different typologies?

2.7.5 Summary The main concept is how to design a programmable cyberspace for multidisciplinary functions and users that can be upgraded like a machine and as smart as an AI application that is architecturally optimized and robotically fabricated.

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Chapter 2 : Optimization of Architectural Spaces

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Figure 2.20 Thesis Framework

2.7 Framework The work presents a customized production framework which is informed through digital optimization methodology that consists of virtual data cloud system. In this system, every part of data gives a different type of information and every part has its characteristics of physical or non-physical properties.

The framework that is presented here is examined through using optimization method to produce 1:1 robotically produced prototype of a multi-layering hybrid component. So, the digital model is divided into different layers that will be produced using the robot. A closed feedback loop is produced so we make use of all the available data.

Existing Context Location Data Weather Data Existing Buildings Typology

Architectural Program Function Space Quality

Prototype

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Chapter 2 : Optimization of Architectural Spaces

Data Analysis

Macro Scale

Meso Scale

Micro Scale

Spatial Ogranization User Circulation

Infromed Design Design Factors Fragmentation

Parameters Agent-Based Design Materiallization End Effector Clay Printing

Customization

Lighting

Alternatives

Structural Analysis

Optimization

Heating & Cooling

Experimentation

Ventilation

Hybridity Design Matrix

Constrcuction

Feedback Loop

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Chapter 3 Methodology

DATA-Driven Design

Description This chapter shows the main methods that has been applied to the research flow starting from the main framework and design methods to data analysis and case study assignement.

3. DATA - DRIVEN DESIGN 3.1 Digital Design The data revolution is altering every field of work and life-including building construction. Architecture has always reflected the cultural and commercial forces of its era; today the most dominant force in society and industry is the duplication of information resources.

Big Data; is so large information that requires new techniques just to manage the volume as it exceeds the capacity of traditional knowledge management strategies.

The use of code, scripts and parametric descriptions is a potential simplification in this process of communicating a design between different teams and specialization. Numbers language is the engine for all of that qualitative process in which codes are clear, precise, readable descriptive algorithms that are potentially accessible across the entire design team and can serve as future reference. In metaphysics, natural phenomena are described by formulas.

While this approach has proven to be difficult to adopt for the explanation of the greater universe, but to harvest building information, it is a valid approach in building design and explore various iterations.

In this area; different aspects of datadriven design will be demonstrated in the building is benefiting from the age of information and how it will evolve in the future. Traditionally; subjective taste; experience; and intuition of designers D-DHS

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were the engine to design. More and more; however; advanced performance parameters and knowledge are changing how buildings are being conceived; developed, and operated.

Nowadays rational; objective standards data-driven techniques are allowing the design process to be driven more by and achieve new heights of performance, and the emerging results are increasingly responsive environments finely tuned to people and place. Digital-based modeling tools and simulations allow us to test various forms via basic functions creating families of designs and topology iterations. Information models are comparable to spreadsheets in their ability to capture the comprehensive data of a building. Every dataset is visualized by one of these digital geometric models and tested by their attributes in the process.

Big Data to super innovation and improving building performance Design can evolve much the way natural organisms do-but dramatically faster but you need to know how to define the objectives and the parameters.

Compared to manual traditional modeling techniques, this approach can serve as a base for further project planning process.

The best way is to reduce complexity to the minimum to maintain the control of geometries for better possible solutions which is more real and readable. For example, simple mathematical equations can generate a wide variety of forms. In

Chapter 3: Data-Driven Design

building design, the simplicity reduces the complexity of the geometric model and allows for a more direct hyperlink to mature planning phases such as area measurements, facade penalization, and manufacture.

Data-driven generative frameworks are colossal in approach and come about, and incorporate, for instance, cell automata, punctuations, and multi-operator frameworks. In this manner, information-driven outline forms progressively incorporate or are connected to, appearance, creation or development forms. This issue centers specifically around the

generative capability of multi-operator frameworks in view of self-association.

Self-association is a procedure in which the association of a framework rises base up from the communication of its parts. Not exclusively can informationdriven craftsmanship and engineering be composed and manufactured by computerized implies, yet they can likewise join data, learning, and detecting activating components that empower ancient rarities from artworks to structures to have constant task and cooperation with situations and clients.

Figure 3.1 Octopus Screen showing various iterations of generating geometries based on the same objectives

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Figure 3.2 Design different data cateogeries

3.2 Optimization of Architectural Space 3.2.1 Design Factors and Parameters According to humans’ needs and natural parameters which are dynamic and changing through time are to control the possibilities of buildings to improve its performance which is called as a term "adaptive architecture".

Space

Design Program

"Correspondingly architectures need to be a living and evolving thing” (Gordon Pask).

Figure 3.3 Data objectives for Architecturak Design

Architectural space is defined by different attributes which can be controlled based on different objective and factors.

Some of these factors are qualitative or quantitative. Qualitative data is the one that is related to the geometry shape and aesthetics of the space and user preferences, otherwise qualitative data is the one related to size, environmental analysis, structural analysis and cost estimation and so on.

To optimize an architectural space towards one of these objectives you need to define which parameters to be controlled and the priority of some factors over the others. For example, you can define the objective of the volume of a rectangle but it x,y,z dimensions and optimize that regarding specific proportions you want for space. So every time you try to change a parameter the other changed directly.

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Context User

Area/Size Function Environmental

Spatial Relations

Users Circulation Function

Structure Cost

Function Context

Aesthetics

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Context Analysis

Environmental Data

Architectural Program

figure 3.4 Data Driven Design

3.2.2 Octopus Multi-optimization In the case study developed for the research work, 3 different parameters are used to generate different iterations of the same program with a vast range of characteristics of every geometry.

Grasshopper plugin Octopus combining with Rhinoceros software is used to evaluate and produce these vary based on same objectives. As shown in (Figure 3.4) there are three objectives which are converted into numbers are used to evaluate the fitness of different parameters. And the optimum is to reach a solution which is near the zero point.

The population of Geometries can be classified depending on the parameters defined as a priority. The better is green to the less is red. Different iteration can be generated from a preferred solution after instating it. The better solution can be achieved based on simple scripts.

Program Types

Unit Sizes

Unit Layout

Spatial Organization

Size of Building

Form and Orientation

Source: http:// livesmarter.weebly. com/blog/datadriven-architecture-aflexible-space

figure 3.5 Octopus Evaluation screen

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Figure 3.6 Octopus 128 Population

3.2.3 Stacking Variations for the Program design Randomly generated from grasshopper depending on the same objectives which are tested based on the proportions and the positioning of spaces according to its relation to the building or to each other using Octopus plugin.

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the proportions of the individual spaces, the volume of the whole program and the distance between spaces according to their relations in the spatial organization

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Figure 3.6 Different generations of Stacking Digrams

Fitness evaluation based on the distance between spaces, so the 3 distances were measured from every space to 3 nearest other spaces, the volume is defined by the bounding box of the whole stacking spaces, an extra parameter is defined is the collision of spaces and others which is more qualitative here.

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Figure 3.7 Mesh from stacking volumes

3.2.4 Geometry genrations from the stacking volumes Another step is to generate different geometries based on these stacking variations and later these also can be evaluated in regards to environmental analysis of every geometry.

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3.3 MACRO SCALE

Geometry Generation

3.3.1Stacking Program Volume An individual volume of the program spaces is defined based on the 4 categories of the space; Incubating spaces, manufacturing spaces, Exhibition spaces and services and a unique color is assigned to every category. The second step is to define the relations of the program and group the volumes based on these relations. Then a volume and distance objectives are tested based on the positioning of the different groups and proportion of the spaces.

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Figure 3.8 24 Selective population of stacking volumes

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3.3.2 Stacking Cloud of points Generating a whole cloud of points of the space based on the range of the designing and try to evaluate this based on the require position we want of the new spaces. This pretty much a quantitative approach and based on aesthetics of the eye.

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Figure 3.19 24 Selective population of stacking volumes in a point cloud

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3.3.3 Selecting Volume intersected cloud of points The step is to define the number of points inside every volume which represents the space size and proportions also excluding the exterior cloud of points.

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Figure 3.10 24 Selective population of Volume cloud of points

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3.3.4 Generating mesh from cloud of points In this step the main concept is to generate a mesh geometry for each stacking cloud of points and define the smoothnes and the denisty of everu one.

We can see that some fo these meshes are not habitable by the nature of shape so this is something to be evaluted based on the typology of the spaces.

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Figure 3.11 24 Selective Meshes generated from stacking points cloud

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Figure 3.12 Selected iteration for the design

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1. Stacking Variation

2. Populating Cloud of points

3. Stacking Volume Cloud of Points

4. Genetrating and smoothing geometry

5. Subtracting the new program form athe old building

6. Combining the old and new geometries

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3.4 Building Design Figure 3.13

Skeleton for the new Program and Spacses

Axnometric view showing the design different parts

Skin for the new Program and Spacses

Skeleton of the old Building

Skin for the old Building

Figure 3.14 Define a part for Meso Scale

Meso Part

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3.4.1 Floor plans Figure 3.15

up

Ground Floor Plan Scale 1:750

Elevator

Exterior Space

Exhoibition

Entrance

Sales office

Manufacturing Space 1 Warehouse

up

Entrance

Break Lounge

Figure 3.16 First Floor Plan Scale 1:750

Quality Assurance

Seminar Hall

Meeting Room

Figure 3.17 Second Floor Plan Scale 1:750

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Incubator & Innovation Space

Manufacturing Space 2

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Figure 3.18 Longitudinal Section Scale 1:750 Incubator Space Manufacturing Space 1

3.4.2 Geometry Morphology The geometry shape has different morphologies which differs based on the function and size of every space. Cutting the Geometry in a different position and trying to analyze the shapes of the outer and the interior space.

Figure 3.19 Cross Section Morphology Layers Scale 1:400

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Figure 3.20 24 Cross Sectional Morphologies Scale 1:1000

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Figure 3.21 Interior Rendering 01

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3.4.3 Renderings This part shows the imagination of how space could be used related to the interior human scale

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Figure 3.22 Interior Rendering 02

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An interior shot showing the spatial organization and the structural morphology of the interior skeleton and the new exterior skin after applying the generative digital approach.

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3.5 MESO SCALE

Ac

ou

stic

hti

Figure 3.25 Meso D2RP&O Data Factors

vir n

Mass Customization

On-Site Construction

Furniture

In this case, this system is a multioptimization cloud of data which defines the physical properties of an architectural space like space defining data, structure analysis data, heating and cooling loads data, required lighting data and smart devices integration for the environment.

All of these data carries an important information that informs the building components design and the robotically produced prototype.

Structure

Technical Porosity

Insulation

Furniture

3.9.1.1 Factors Structure : Wall, Slab, Clumn, Beam Insulation: Heating and Cooling, Acoustics Porosity: Openings, Ventilation, Lighting Furniture; Integrated Structure furniture Technical: Smart Devices, Wiring Network

Wall

Openings

Column Ventilayion

Acoustics Lighting

Smart Devices

Heating & Cooling

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m

Economical

Beam

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Meso Design Data Classification

lt Mu

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iall

r ate

i-M

me nta l

Ve nti lat ion Ins ula tio n

Based on D2RP&O Production experience a different set of factors should be addressed such as Structure, Insulation, Porosity, Furniture, Technical Wiring.

Figure 3.24

ton

ele Sk

s

ng u Str

3.9.1 Optimizing Skin and Skeleton Design In the Mesoscale different Architectural Factors have been considered this time regarding Structural, Environmental and Economic factors.

Lig

En

Architectural Factors

Material Optimization

Figure 3.23

Slab

Wiring

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3.9.2 Programmalble Wall An existing Structure has different attributes and the idea is to create subtraction and addition for the new and existing skins.

figure 3.26 Programmable Wall transition

Addition

Existing Wall

Subtraction

Transition to new Space

Hybrid Material

Defining Porosity

Defining Old & New Parts

3.9.3 Meso Framework Transition To the New Phase

Existing Space

Meso Framework

Requirements for the new function Enhancing the Architectural Factors Evoloving more than Intervention

figure 3.27

Growing Structure Defining Porosity Smart Devices Future Extension Parameters

Percentage of Old & New Percentage of Solid & Porosity Structure Enhancements

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Figure 3.28

3.9.4 Particles Cloud vs. Points Cloud

Meso D2RP&O Data Factors

Existing Construction

Growing Construction

Point Cloud

Particles Cloud

Static Data Seperate Elments Once Generated Equal Weight

Dynamic Data Correlation Regeneration Recursive

Particles Cloud User Behavior Environmental Data Architecture Program Context Analysis Design

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Density and Size

Circulation Network

Layout Design

Material Selsction

Circulation Network

Construction Constraints

Context Analysis

Layout Design

Location Data

Circulation Network

Architecture Program

Spatial Oganization

Spaces program

Openings Design

Environmental Data

Form Orientation

Weather Data

Size of the Building

DIA SS16/17 Meso Cloud of Points, Topological Hybridity

Spatial Organization

Form Outline

Figure 3.29

User Behavior

Materila Slection

Agents Swarming

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3.6 Voxelization "A voxel speaks to an incentive on a general network in three-dimensional space. Likewise with pixels in a bitmap, voxels themselves don't commonly have their position (their directions) expressly encoded alongside their qualities".

Rather, a voxel depends on its position in respect to different voxels in the information structure to make a greater 3d picture. then again focuses and polygons are regularly unequivocally spoken to by the directions of their vertices.

mistiness, or different bits of information, for example, a shading notwithstanding darkness. A voxel speaks to just a solitary point on this matrix, not a volume; the space between each voxel isn't spoken to in a voxel-based dataset. Contingent upon the kind of information and the expected use for the dataset, this missing data might be reproduced as well as approximated, e.g. by means of addition.

3.6.1 Minimal Surfaces Voxels Trying to analyze how minimal surfaces voxels are working to reproduce and create larger continuous structures.

A voxel speaks to a solitary example, or information point, on a routinely divided, three-dimensional matrix. This information point can comprise of a solitary bit of information, for example, a Figure 3.30 Minimal Surfaces Voxelization

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3.6.2 Voxel Cells Types A family of voxels is created according to different D2RO&O factors and data trying to create different families based on the geometric shape of the voxel, the reproduction of every in the 3d space and the way of the materialization of different voxels.

Some of the voxels are considered as structure voxels. others for porosity, others for Insulation, some voxels are for furniture which can be flexible materialized and at last technical voxels respecting the meso design factors for architectural space optimization.

Solid: 50 % Void: 50 % Porosity: 00 %

Solid: 50 % Void: 50 % Porosity: 00 %

In Regards of robotic materialization, some aspects have been considered too as the assembly logic continuity of voxels and the amount of material used and the percentage of cavity and porosity included.

Solid: 50 % Void: 50 % Porosity: 00 %

Solid: 50 % Void: 50 % Porosity: 00 %

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Figure 3.31 Voxel Cells Types

Solid: 50 % Void: 50 % Porosity: 00 %

Solid: 70 % Void: 30 % Porosity: 00 %

Solid: 30 % Void: 20 % Porosity: 50 %

Solid: 60 % Void: 40 % Porosity: 00 %

Solid: 70 % Void: 30 % Porosity: 00 %

Solid: 50 % Void: 20 % Porosity: 30 %

Solid: 40 % Void: 30 % Porosity: 30 %

Solid: 30 % Void: 40 % Porosity: 30 %

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Figure 3.32

l tra

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Voxel Matrix

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Figure 3.33 Meso Voxelization (Iteration 01)

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3.6.3 Hybrid Voxels Generations Depending of the 12 factors of the Meso part 12 families can be generated to populate the meso part. So evey wall can be made of different sizes and types of cells depends on the optimization factor of every voxel and the position related to othee voxels.

Figure 3.34 Voxel Matrix

This introduces us to the concept of hybridity of construction materialization and design.

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Figure 3.35 Hybrid Meso Voxelization 02 (Iteration 02)

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01. Meso Basic Surfaces

02. Stress Lines anlysis

03. Cells Cloud

04.Deforming Cloud

05. Cells Selection

06. Voxelization of Different parts

07. Structure Voxels (Part 01)

08. Porosity Voxels (Part 02)

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Figure 3.36 Hybrid Meso Voxelization 02 (Iteration 02)

01. Meso Basic Surfaces

02. Stress Lines anlysis

03. Cells Cloud

04.Deforming Cloud

05. Cells Selection

06. Voxelization of Different parts

07. Structure Voxels (Part 01)

08. Porosity Voxels (Part 02)

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Chapter 4 Procedure & Applications

Hybrid Materialization

Description This chapter has a lot of work spreading from theoritical and design theory to physical work and experimenting through robotic fabrication and 3d printing technology showing different approaches to apply multi-materiallity concept to architectural spaces for space quality enhancements.

4. HYBRID MATERILAIZATION

Figure 4.1 Meso Part Design

4.1 Digital Driven Materialization 4.1.1 Meso Materialization In our case this system is a multioptimization cloud of data which defines the physical properties of an architectural space like space defining data, structure analysis data, heating and cooling loads data, required lighting data and smart devices integration for the environment.

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Figure 4.2 Meso Data generations

4.1.2 Data Generations and Relations All of these data carries an important information that informs the building components design and the robotically produced prototype.

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Figure 4.3 Meso Cloud Iterations

4.1.3 Meso Iteration All of these 12 data optimization factors are transformed into different variations of cloud system for applying a different kind of voxels to it to have multi- hybrid mesoscale.

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4.2 Meso Hybridity 4.2.1 Data Cloud In this part a selection and categorization system is applied depends on the percentages of the 12 factors to sort the voxels and classify the data. Different iterations can be made by mapping vast range of user interface and function changing.

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Figure 4.4 Meso Data Cloud Iterations

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4.2.2 Cells Cloud In this part, a cells cloud was produced with different sizes according to the data weight, function, and required surface design.

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Figure 4.5 Meso Cells Cloud Iterations

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4.2.3 Multi- Hybridity Meso Voxelization In this part, a cells cloud was produced with different sizes according to the data weight, function, and required surface design.

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Figure 4.6 Meso Voxels Iterations

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Figure 4.7 Meso Part 3d Section

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Figure 4.8 Meso Multi Hybridity Part 3d Section

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Figure 4.9 Meso rendering 01

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Figure 4.10 Meso rendering 02

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Multi-Materiality Matrix

4.3.1 Multi-Materiality Conducting an analysis of different case studies that address this idea of multi-materiality is the first step in understanding how to develop it through additive or subtractive techniques. The integration of two different materials in designing a structure element was the base to enhance the structural characteristics of concrete. So maybe this could the base for using the local materials to help in to achieve building complex forms which would be more related to its environment.

Materialization is a relative term in architectural D2RP&O. Some materials may be considered as combinations of the two. The best way of materializing an architectural part is to be based on different parameters that control the design of the material. Multi materiality is one version of that thing to have two different parameters that control the percentage of the materials and the characteristics of each. Every material has different composition and Structure which can be enhanced by other materials.

Materials are distributed following properties and behaviors based on multiple design objectives. The project introduces two different clay materials to improve structural performance and acoustic performance of the building envelope.

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Variable 01

Variable 02

Figure 4.11

Di m en sio n

4.3 MICRO SCALE

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4.3.2 Addidtive Manufacturing There are in fact a number of different subtypes of additive manufacturing including 3D printing. Recent advances

in this technology have seen its use become far more widespread and it offers exciting possibilities for future development. figure 4.12 Additive manufacturing matrix

Pattern

Cell volume

Density

Orientation

1D

2D

3D

Fibres

Layers

Voxels

4.3.3 Hybrid Techniques Analyzing the different ways of additive manufacturing, it shows that there is some parameters that can be included like the size of printing and the 3d

printing code and technique limits and advantages. Hybridity between different production techniques involving additive and Subtractive methods. figure 4.13 Hybrid Techniques

Multi-Material 3d Printing

Contour Crafting

Subtractive Manfacturing

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4.3.4 Volumetric tessellations The concept of volumetric tessellations is to have two different isotropic surfaces which created from one tessellation and also has a volumetric interweaving between different layers Figure 4.14 Tex Fab Hybrid Plasticity Volumetric Tessellations

4.3.5 First Experimentation The same idea of volumetric tessellation was applied to have two different layers with an interlocking system and to have anisotropic surfaces.

Figure 4.15 Interlocking Geometries

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4.3.6 Linear Differential Growth Differential growth is a feature of cells, the organs which they construct, and the whole plant itself. The control of differential growth at each of these three levels of the organization resides in the level lower than that in which it is expressed.

4.3.7 Linear Simulation

Differential

Figure 4.16 Linear Differential Growth

Growth Figure 4.17 Hybrid Techniques

4.3.8 Hybrid Differential Simulation on Shell Structure

Growth Figure 4.18 Hybrid Techniques

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4.3.9 Interlocking Grids In this experiment the idea was to try to produce two different grids which are isotropic 3d grid made of modular voxels and use the concept of the hybridity to be applied by 3d printing every isotropic part with different materials and interlocking them after to get a solid surface.

Also, The concept of voxelization was also applied to this experiment to give the surface some structure enhancements, porosity and tectonics.

Figure 4.19 Hybrid Interlocking 3d Grid

Part 01

Part 01

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Figure 4.20 3d Printed Hybrid Interlocking Grids two Parts

Figure 4.21 3d Printed Hybrid Interlocking Grids Assembled

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4.4 Voxel Materialization 4.4.1 Surface Tectonics and Porosity By applying different techniques of Experimenting with the voxel design to enhance the surface tectonics and to create the idea of hybrid materials to be printed together using Robotic extrusion or normal 3d printing. Also Trying to give the surfaces some porosity and interesting patterns. Also trying to make the use of the way of the 3d printing dimensions which could be 1d fiber in case of clay extrusion or 2d with normal 3d printing.

Figure 4.22 Voxel Tectonics and Porosity 01

Iteration 01

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Iteration 02

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Figure 4.23 Voxel Tectonics and Porosity 02

Iteration 03

Iteration 04

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4.4.2 Hybrid Voxel Materialization By applying the concept of linear growth simulation on voxels surface in 2 different iterations. First is to create two different discrete surfaces and the second is to be applied for surface tectonics. Then a process of attraction has been applied to deform the grid and create some porosity using repulsion and increase material at some points using attractions. Also, this was experimented using the normal PLA 3d Printing and would be printed using robotic clay extrusion.

Figure 4.24 Voxel Tectonics and Porosity 03

Iteration 01

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Iteration 02

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Figure 4.25 Voxel Tectonics and Porosity 04

Iteration 03

Iteration 04

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Figure 4.26 Voxel Tectonics 3d Printing

Figure 4.27 Hybrid Voxel and prorosity 3d printing

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Figure 4.28 Production Process of Two Hybrid double curved Surfaces

4.4.3 Digital Prototyping First, the voxel has to be cut into small reachable by the end effector to be 3d printed. The parts are decomposed into small isotropic surfaces which the linear growth method is applied to give it tectonics and porosity.

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Figure 4.29 Digital Hybrid Prototype

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Figure 4.30 Digital Deformed Hybrid Prototype

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Chapter 5 Conclusion

Robotic Prototyping

Description A summary for the research work has been included in this chapter provideing a clear conclusion for the 3d printing methods and further future approaches for more experimenting and research.

5. ROBOTIC FABRICATION 5.1 Robotic Prototyping 5.1.1 Architecture Digital Prototyping Since this topic is one of the recent emergent science in the field of architecture dealing with digital fabrication. Also, many researchers have approached have approached this topic from many different perspectives.

Though studying and analyzing multiple case studies would be very useful for understanding the potentials of the technology and how to apply it in Architecture. Conducting an analysis of different case studies that address this idea of multi-materiality is the first step in understanding how to develop it through additive or subtractive techniques. The integration of two different materials in designing a structure element was the base to enhance the structural characteristics of concrete. So maybe this could the base for using the local materials to help in achieving building complex forms which would be more related to its environment The combination of creative innovation in compositional plan advances decentralized methodologies underway procedures and encourages masscustomization.

The open-source calculation in plan and creation prompts the democratization of manufacture schedules, successfully enabling the two fashioners and clients to get to and work modern apparatus on request.

In the work presented here, the digital fabrication of space voxels hybrid

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structures is further explored with a hybrid production approach by combining two methods of production approach.

Whereas multi-robotic material manipulation is well-established for programmed tasks in production lines.

The multi-system robotic setup concentrated on advancing creation work process for additive manufacture and added substance creation. It joined two additive manufacture procedures for prototyping, a volumetric assembly for quick material expulsion and fabrication processing to include additional points of interest and porosity.

Chapter 4: Hybrid Materilaization

5.1.2 Robotic Experimentation The first robotic experiment was extruding a deformed grid on a flat surface using flexible silicon material applying different speeds of robotic movement. then the experiment was left to dry and then is used like a flexible surface.

Figure 5.1 Silicon Robotic Printed Prototype

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5.1.3 End Effector The research is tested through robotic additive manufacturing so building a customized 3d extruder for the KUKA robot was the perfect way to have an actual prototype. A simple design was made based on Arduino UNO chip and stepper motor using some 3d printed parts for the nozzle and laser cut parts for the frame which is made from plexiglass and steel rods. Then a wooden connection was made to attach the extruder to the robot.

Testing extruder wooden connector and attaching it to the robot head using bolt and brackets and also adjusting the direction of it to the home position.

Figuring the right wiring of stepmotor to the drive and the UNO Arduino then test the speed and the direction of the step motor rotation.

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Complete the assembley and make the extruder ready to work for the TCB calibration and positioning.

Assembling the base of the extruder; frame and materila tube then test the weight and the movemnet to make consistent and precious.

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Figure 5.2 Materials for Building the Extruder

Load the code from the grasshopper to the robot and start extruding the material and printing.

Load the material (silicone) and test the extrusion manually and tuning the extrusion speed to the movement of the robot.

First layer finished we were ready fo next layer checking resolution, nodes sizes.

and the the and

Figure 5.3 Extruder Testing and producting first Prototype

Monoitoring the movement and the extrusion and write the notes for next layer

Feedback Loop

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Figure 5.4 Extruder Design

Figure 5.5 Extruder robotic Fabrication

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Figure 5.6 Milling the double curved surface

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Figure 5.7 Material Extrusion on the double Curved Milled Surface

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5.2 Conclusion The research tried to address the possibilities of informing the design to the robotic production process of designing and manufacturing architectural emerging space through different numbers of data sets of design objectives controlled by some space, structure, environmental, parameters that inform the design and the actual prototype. Trying to optimize that through some simulations and structural analysis.

A framework for generating design from these data was conducted to produce a vast range of prototypes for various factors. So developing one process but a bunch of prototypes. The framework that is presented here is examined through using optimization method to produce 1:1 robotically produced prototype of a multi-layering hybrid component.

So, the digital model is divided into different layers that will be produced using the robot. A closed feedback loop is produced so we make use of all the available data.

Also using 3d printing prototyping to test some of the experimentation in different methods of printing, so it is possible to get different feedbacks to inform and update the data set for the design in every Macro, Meso and Micro level to inform the design and enhance the architectural Factors of the spaces on the different scales.

the design and the production of architectural constructed hybrid parts and assembly. This is implied in the feedback loop between the robot and the design to update the data and reach the optimum design for architectural spaces parts.

5.3 Further Investigations For the future research, a group of evaluation simulation should be applied to narrow down the results and more mapping to be applied to define more accurate data and voxel positioning for 3d printing. Also, it is recommended to develop a real-time simulation for optimization and updating the data directly.

For the prototypes, some data would be collected for the robot experiment regarding the fabrication phase like the speed and the method of manufacturing.

More Robotic testing is following the research and to be addressed later.

Prototypes material feedback should also be conducted towards enhancing the process and specify the architectural Attributes.

It is proven that data analysis and classification very help to inform

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VIII. Bibliography 1. BOOKS [1.1] B. Pine II, Joseph, Mass Customization; The New Frontier in Business Competition, Harvard Business School Press, 1993.

[1.2] Gramazio and Kohler. The Robotic Touch:How Robots Change Architecture .

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[1.3] Gramazio and Kohler. Digital Materiality in Architecture . Baden, Switzerland: Lars Muller Publisher, 2014.

[1.4] Gramazio and Kohler. Digital Materiality in Architecture. Baden, Switzerland: Lars Muller Publisher, 2014.

[1.5] D. Reinhardt, R. Saunders, J. Burry, Robotic Fabrication in Architecture, Art and Design. Springer, 2016 [1.6] A. Hidding, Continous Exploration Master Thesis, TU Delft, 2016 [1.7] A. Menges, B. Sheil, R. Glynn, M. Skavara, Fabricate, UCL Press, 2017 [1.8] Hao Li, Wenyan Zhao, Jialin Tang, Xinnan Zhao, Zizhuo Su, Heyoung Um, Jiawei Xi, Xiangheng Min BPro RC 5+6 _Clay Robotics-Catenoid Aggregates, UCL, London, 2016/17 [1.9] J. Hargrave, L. Goulding, ARUP, Rethinking Factory, London, 2015 [1.10] Dgifab Turino, AARM, Algorithmic Art Robotic Material, 2017 [1.11] B. Farahi and N. Leach (eds.), 3D-Printed Body Architecture. Wiley, London, 2017 2. PAPERS H. Bier and S. Mostafavi. Structural Optimization for Materially Informed Design to Robotic Production Processes: American Journal of Engineering and Applied Sciences, 2015.

[2.2] H. Bier and T. Knight, Data-Driven Design to Production and Operation. Footprint.

1-8. 10.7480/footprint.2.807.

[2.3] H. Bier and T. Knight, Digitally-Driven Architecture. Footprint · January 2010.

[2.4] N. Leach, A.s Carlson, B. Khoshnevis and M. Thangavelu, Robotic Construction by Contour Crafting:The Case of Lunar Construction. IJAC. issue 03, volume 10 [2.5] G. Cesaretti a, E. Dini , X. DeKestelier, V. Colla ,L. Pambaguian. Building components for an outpost on the Lunar soil by means of a novel 3D printing technology, Acta Astronautica 93,(2014)430–450.

[2.6] E.L. Doubrovski , E.Y. Tsai , D. Dikovsky , J.M.P. Geraedts , H. Herr , N. Oxman, Voxelbased fabrication through material property mapping:A design method for bitmap printing, Computer-Aided Design 60, 2015.

[2.7] S. Mostafavi, A. Anton, S. Bodea Design to Robotic Production for Informed Materialization Processes, TU Delft, 2017.

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3. WEBSITES [3.1] K. Rosenfield, "Justin Diles Wins TEX-FAB Plasticity Competition with "Plastic Stereotomy"" 05 Nov 2014. ArchDaily. Accessed 26 Dec. 2017. https://www. archdaily.com/564444/justin-diles-wins-tex-fab-plasticity-competition-with-plasticstereotomy/ [3.2] N. Azzarello, "SHoP architects' enormous 3D printed pavilion to mark design miami 2016 entrance". 07, 2016. Designboom. Accessed 23 Dec. 2017. https:// www.designboom.com/design/design-miami-shop-architects-3d-printedpavilion-10-07-2016/ [3.3] R. Rael, V. San Fratello, K. Wilson, A. Schofield. "GCODE Clay" . Emerging Objects.

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