3Dprint.AI, Towards open-source, large-scale 3dprinting beyond intelligence

Towards open-source, large-scale 3dprinting beyond intelligence

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Abstract At the threshold of the digital revolution in fabrication, emerging technologies such as additive manufacturing are dramatically transforming both design and making (Gershenfeld, 2015). However, the current limitations of additive manufacturing regarding cost, time and quality are limiting its future development, especially its implementation in large scale. From the abovementioned issues arises the hypothesis of this thesis, namely, the technological advances in machine learning algorithms and the open innovation in open source communities have the potential to revolutionise large-scale additive manufacturing, enabling it to shape the future of making. To test this hypothesis, this thesis introduces 3Dprint.AI, an open source machine-learning framework for large-scale additive manufacturing, which aims to engage users and technology in the application of additive manufacturing at an architectural scale in a sustainable and affordable way.

3Dprint.AI operates in two modes ,the manual and the automatic one.It involves the following steps i) video recording and object recognition while printing, ii) adaptive feedback to the robot, iii) documentation of all printing settings and results, and iv) distribution of those settings either via google sheet files-manual mode- or through an online distributed network to enable their use by machine learning algorithms to make predictions-automatic mode-.3Dprint.AI is implemented in design and development of a high-quality extruder, which entails the integration of the above steps in manual mode . Printing tests are conducted to create data sets for use to predict the extruding outcomes and train machine learning algorithms of the community. The thesis concludes by proposing software development as an additional operation to the above open source framework, which can save computational power and earn an income for the community.

2019. Georgios Drakontaeidis.All rights reserved.

All rights reserved. No part of this thesis may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

Agenda This thesis below is done in Research Cluster I (RC1) at the Bartlett school of Architecture ,2019. The aganda of RC1 for the academic year 2018-2019 is the following: ‘‘This year, Research Cluster 1 will explore automated design and fabrication systems, with a focus on robotic 3D printing technologies and their application at an architectural scale. In collaboration with industry partner, Robotic 3D printing company Ai-Build, we will challenge the current state of the industry. We will study 3D printed matter not only as the final product, but also as a mould or substrate for a multi-material system. In parallel to this, students will develop highly adaptable design systems, which are linked to fabrication processes and environmental datasets’’ (The Bartlett School of Architecture, 2019).

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Figure 2. Industrial robots, production line.

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01| Introduction 1.1 Definitions

1.2 Hypothesis

‘’Machine Learning (ML) is the practice of using algorithms to parse data, learn from it, and make a determination or prediction. The machine is “trained” using large amounts of data and algorithms .’’ (Copeland, 2016).

Gershenfeld (2012) declared the coming of a new digital revolution in fabrication, while Bitonti (2016) rejected the claim stating that the full potential of technologies such as AM had barely begun to be discovered. Despite its apparent prospects, the lim-

Additive manufacturing (AM) is the process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies(Wohlers Associates, 2010).

Robotic AM is an additive manufacturing process using industrial robots due to their high speed, accuracy and freedom of movement in multiple axis and large scales. In this thesis, the term large-scale AM is entirely referring to robotic AM.

itations of AM are confining to a prototyping scale.

Bitonti (2016) noted that even though AM has the potential for large scale application, the industry uses it only as a prototyping technology due to its limitations.

Anderson (2102) demonstrated that the open innovation in both hardware and software can empower current technologies like AM in a global Maker’s framework. However, even though the computational power and algorithmic availability make the application of machine learning to AM promising, it remains a conceptual and technical challenge.

To overcome these obstacles, this thesis introduces 3Dprint.AI, an open source machine-learning framework for large-scale additive manufacturing, which can accelerate its development and minimize its limitations. The 3Dprint.AI framework has been created at a time when AM is facing numerous limitations in making substantial advance to challenge, intensively and decisively, all other subtractive manufacturing techniques.

This thesis is motivated by the idea of bringing AM to an architectural scale by creating an online platform where users and labs may exchange data and knowledge about hardware and software for implementing AM on a large scale. However, this thesis aims not at creating the platform but at studying the challenges posed by the framework in which the platform would operate.

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1.3 Method The proposal for a machine-learning framework for large scale AM draws upon research in several related fields. Bringing together the Makers culture of DIY-ism in open hardware and software, the machine learning trends in digital design, and the use of robotics in digital fabrication, this thesis proposes a framework with the potential to raise AM to architectural scale by challenging the current state of the industry.

The analytical axis of the thesis revolves around the questions – Can open source innovation and machine learning algorithms advance large scale AM? The critical angle of the thesis is centred on the questions of whether an open community for large scale AM is possible. Are there any ethical issues, apart from technical ones that prevent users from participating? Will users trust a blockchain network for distribution of data or the high skills required for participating in the community and yet maintain a degree of inaccessibility to it? How will the financial needs of the community be met efficiently?

As an alternative to bibliographical research for finding answers to these questions, two polls, one at the Autodesk University conference 2019 on ‘the future

of making’ was taken after the idea of this thesis was presented and discussed in detail and the othre from Bartlett students. Even though the results of the poll cannot be totally objective since the limited number of participants, the responses to the poll illustrate the thoughts,of the possible members of the community.

Finally, most of the 3Dprint.AI operations were implemented manually, using an extruder developed for all the students of Research Cluster I. The creation of the community was criticised using the extruder outcomes of other clusters at the workshop. The above design of the extruder was given to other students who quickly gained the skills of spatial printing in the conducive intellectual environment and exchanged designs and data in the workshop.

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Figure 3. Steel 3dprinted bridge,MX3D.

1.4 Steps

1.5 Intended contributions

The thesis is developed in the following steps:

This research intends to engage three different groups of readers. For architects - design researchers, it aims to problematize on the existing limitations of digital fabrication and illustrate the potential of creating an open source community for applying machine learning in AM for pushing both design and fabrication out of their current limits. For researchers in artificial intelligence and programmers, this thesis aspire to inform them about the problems of AM and motivate them for the implementation of machine-learning algorithms in AM in order to revolutionize it. For makers and thinkers, it aims to demonstrate a framework for making things in large scale using robotic AM while clearly highlighting the challenges, dangers and prospects of it.

Chapter 2 will frame the content of the thesis historically while debating the main points of the framework. Trajectories in large scale AM, in machine learning and in open source communities will be analysed and criticised.

Chapter 3 will introduce and the 3Dprint.AI framework in manual and automatic mode. It will analyse the organisation of community and data. It will present both technical and ethical challenges and analyse plans for its economic efficiency.

Chapter 4 will introduce the extruder, which was developed for RC1 by implementing most of the steps of the framework in the manual mode. It will evaluate the experience from the B made workshop of Bartlett where all the designs were given to the students from other clusters.

Chapter 5 will evaluate and criticise the research described in this thesis and, conclude by laying out the path for the future development of the framework.

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Figure 4. Daedalus Pavillion detail,Ai build,2016.

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02| Background 2.1 Problem statement The problem statement of the thesis is based on the problems that have persisted historically in AM implementation on a large scale; though it is continuously gaining momentum at the prototyping scale. At the same time, according to Carpo (2017) machine learning algorithms have been developed and successfully applied to many different fields from digital design to fabrication. Furthermore, Anderson (2012) has contended that the open source communities have revolutionised codes and systems and online interaction among the users can lead to great innovations. Consequently, this thesis tackles the question of whether open innovation and AI algorithms can be applied to large scale AM and accelerate its development.

This chapter frames the above questions by studying the historical trends in the developments in DIY, open source and Ai up to the 21st century. After debating the main points of the framework, the chapter continues by studying and critically discussing the trajectories in large scale AM, machine learning applications in design-fabrication and the innovations made by specific open source communities.

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2.2 Historical and social background According to Carpo (2017), the shift from traditional customised hand-made objects to standardised machine made things occurred in the 19th century during the first industrial revolution when economies of scale, that is the fact that the price of the products is in inverse proportion to the scale of production, were noted. Hermann, Pentek, and Otto (2016) noted that from the 1870s the electrisation of production and new models for the organisation of human labour shaped the second industrial revolution (Figure 8). In that period, the construction industry could not construct complex buildings and architects introduced the modular system during the 1930s in order to rationalise and simplify building design (Chaillou, 2019). In 1952, successful control of a milling machine by means of a computer at MIT was the beginning of what is called digital fabrication (Gershenfeld, 2012). During the 1950s, the current technological advances, prompted Alan Turing to publish the first paper on artificial intelligence – as an imitation of human intelligence – in which he tackled the question of whether machines could be equipped to think. The first industrial robot – the Unimate(Figure 5) – was manufactured in 1955 (Hebron, 2016). During the 1960s, Cristopher Alexander also addressed the question how computers could be used to create design (Chaillou, 2019).

Figure 5. Unimate, the first industrial robot in 1955.

Figure 6. Urban 5,Architectural Machine group, MIT.

At the turns of 1970s , the development of electronics and information technology amplified the effectiveness of production processes and shaped the third industrial revolution or the digital era (Hermann, Pentek, and Otto, 2016). Consequently, the use of computational methods in both industry and design began (Carpo, 2017). For instance, the architectural Machine Group at MIT (Figure 6) investigated how a machine could enhance the creative process and architectural design as a whole (Negroponte, 1972). Until the end of the 1980s, artificial intelligence (AI) remained only at the theoretical level due to the lack of the practical applicability of AI (Hebron, 2016). In that period, the first additive manufacturing machines were developed but due to the patent restrictions they remained unreachable to designers for the last three decades (Gershenfeld, 2012).

In the closing decades of the 20th century, the rise of the internet gave birth to open source programFigure 7. Inventions and innovations in Architecture,introduced by Chaillou in Harvard Graduate School of Design,2019.

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Figure 8. History of industrial revolutions.

ming. It had a strong political, philosophical, and activist slant and many supporters (Brasseur, 2018). At the same time, the ethical questions raised by open source programming and artificial intelligence created an opposing trend which highlighted the issues and negative effects of these technological advances (Brasseur, 2018). In the 1990s, the rise of the web proved that the network was important because the open source communities were now numerous and they made real innovations-ex. Linux that increased their popularity dramatically (Anderson, 2012). In that period, according to Chaillou (2019), paramet-

ricism – a trend in architectural design mastered by Zaha Hadid architects – brought complex shapes within control and made their construction feasible (Figure 9).

Hermann, Pentek, and Otto (2016) have noted that from 2011, academic, political, and business circles are discussing whether the current radical technological advances such as AM, robotics, artificial intelligence and others are shaping a new industrial revolution, the fourth one (Figure 8). During this period, a crucial fact was the end of patent restrictions for additive manufacturing. Anderson (2012)

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has explained that phenomenon as the born of the ‘Makers’ movement (Figure 10), according to which the existing DIY culture mated with the open source web, and both, based on digital design, were applied to manufacturing. Gershenfeld, (2012) has further described these changes as a new digital revolution in fabrication though he has also clearly highlighted its threats such as the theft of intellectual property in open source communities and the production of guns locally (Figure 11).

Figure 9. Heydar Aliyev Center by Zaha Hadid Architects.

Figure 10. Makers manifesto Figure 11. 3d printed gun. book.

Figure 12. Annual supply of industrial robots wordwide and forecast,source international federation of robotics (IFR).

The advances in artificial intelligence, the expansion of computational power and the wide use of industrial robots (Figure 12) have created new potentials as well as challenges-ssues for both digital design and fabrication (Carpo, 2017). Chaillou (2019) has described the advent of artificial intelligence in architecture as refinement of architectural means and methods in a historical sequence of modularity, computational design, and parametricism, which aims to provide greater freedom to design and making(Figure 7). On the other hand, all these advances are creating vital economic, ethical, political and scientific questions such as authorship, ownership and copyright (Picon, 2016); because they radically change the roles of design profession and manufacturing and question classic legal and ethical structures.

Also, Anderson (2012) demonstrated that the above changes in fabrication are inverting the economy of traditional manufacturing, as the concept of economies without scale which was firstly developed in the 1990s by designers (Carpo,2017) is now a fact.

This is because, in digital fabrication, complexity, variety, and flexibility are freed from the need for standardisation of products (Figure 13).

In this framework, many start-ups such as Ai build; institutions like the Bartlett; companies like Autodesk, fab labs; and digital fabrication workshops have started experimenting with large-scale AM in an attempt to overpass the huge limitations of cost, time and quality inherent in non-standard, unique product production.

Figure 13. Economies without scale diagramme comparing to traditional manufacturing.

2.3 Debates on the main idea of the framework After discussing the historical and social framework of both open source and artificial intelligence, it is important to consider if these two main ideas can be merged to raise AM to architectural scale. On the one hand, it can be assumed that the computational power and the algorithmic innovation in the post-digital era can lead to great innovation using AM. However, on the other hand, Hebron (2016) has pointed out that machine learning algorithms need to be explored more intensively because, according to the researchers, they are still at a primitive stage.

Moreover, although AM tends to be a source of innovation, the cost and the complexity of the process prevent people from experimenting with it (Rayna, Striukova and Darlington, 2015). Indeed, learning the skills required for using 3d printers or robots tend to be time-consuming and effort intensive.

Consequently, users tend to quickly lose interest in such machines, preferring not to use them. However, digital platforms can help overcome these difficulties since, most of the time, they enable multiple interactions between users to exchange knowledge and ideas on specific issues and quickly gain skills (Srnicek, 2017). Many digital companies such as Google, service start-ups such Uber, and manufacturing industries, such Siemens have started using platforms for informing customers about their services and products, to instruct them and make their services and products more accessible to people (Srnicek, 2017).

Furthermore, an open source machine-learning framework for large scale AM tends to be a technologically complex and computationally intensive procedure. Therefore, the technical problems that can arise at every step in the process of its creation can make the framework non-functional and just a waste of time and energy. However, Pearson (2016) has noted that the governance inside open source communities can distribute the responsibility of resolving the technical issues among the community’s users for converting a concept into a functional

model since the flexibility of open source environments enables easy and fast modifications of the system.

However, it may be assumed that the emerging ethical questions over the loss of intellectual property within the open source community, the unreliability and corruptibility of the systems it produces, and the lack of ethical-economical rewards for the users are becoming serious impediments in attracting wider participation in the community. In addition, the possibility of the use of the technology in unexpected and often illegal ways – such as unauthorised development of guns – can endanger public safety.

However, Pearson (2016) argues that in open source environments there are specific rules of hierarchy, protection of data and evaluation of users to ensure the ethical and legal aspects of the communities.

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Figure 15. AiMaker, extruder for autonomous decision making,Aibuild.

2.4 Trajectories 2.4.1 Trajectories in large scale AM The following examples are selected through the lens of development of specific technologies that can be used or transferred to the 3Dprint.AI framework such as advance sensing and autonomous decision making.

Figure 16. AiCell, enclosure for industrial robots for stable environmental conditions while printing, Ai build.

Figure 17. AiSync software for strategizing toolpaths using machine learning algorithms, Ai build.

The start-up Ai build has implement studies on AiMaker (Figure 15) ,AiCell (Figure 16) and AiSync (Figure 17 ) aiming to make possible high quality and affordable large scale 3d printing (Ai Build, 2019).

More specifically, AiMaker is a robotic end-effector that attaches to robotic arms and prints at high speed with great accuracy while detecting issues, and makes autonomous decisions (Ai Build, 2019).

AiCell is an enclosure for the robot and aims to create stable environmental conditions during printing (Leonidou, 2019). AiSync is software for strategizing the extrusion toolpaths using AI algorithms Even though AI build has developed significantly largescale AM, its implementation remains on a pavilion scale such as the Daedalus (Figure 18) and Cloud Pergola (Figure 19) due to the high price of the process and its use being limited to handling common PLA thermoplastic materials.

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Figure 18.Daedalus Pavilion, 3d printed architectural installation, Ai build 2016.

Figure 19.Cloud Pergola pavillion, Croatian exhibit at La Biennale di Venezia 2018 ,Ai build 2018.

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Figure 20. 3D printed steel bridge to be installed in Amsterdam at 2019,MX3D.

Another start-up in the Netherlands, the MX3D, has significantly revolutionised the materiality of large scale AM (Figure 20) since they have developed an extruder(Figure 23), which prints metal (Figure 22).

Furthermore, they have developed their own printing software, a welding machine for the robot and they can extrude materials ranging from aluminium to strength steels (MX3D, 2019). They have printed architectural elements for a steel bridge of curved design(Figure 20) soon to be in Amsterdam in 2019 (MX3D, 2019). However, though the results are high quality, the process is slow and costly. Also, the mechanical properties of metal change slightly during extrusion. Further research on metal 3d printing is needed for its large-scale application.

Figure 21.Part of the steel bridge.

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Figure 22. MX3D robot while printing metal.

Figure 23. Extruder of MX3D for metal 3D printing.

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More significantly, Relativity, a US company, recently achieved the most advanced large-scale metal printing by designing and manufacturing ready-to-fly metallic rockets and their engines(Figure 24), in less than two months (Relativity Space, 2019). They use industrial robots with an extruder, which they developed, advanced sensors and software using machine-learning algorithms for reward function-based learning (Relativity Space, 2019). The robots can adapt their moves according to the sensors’ signals (Figure 25) to achieve high-quality working industrial engines in a single process that bypasses addition of assemblies and sub-assemblies as in the traditional manufacturing(Figure 26). By splitting the engine in big geometries and using automation, the company saved time, labour cost, and managed to remain competitive against traditional manufacturing companies to make a significant advance into the world of large-scale AM (Johnson, 2019). The CEO of the company Tim Ellis has declared the technology of the company can be used in other industries, e.g., architecture to change the built environment (Johnson, 2019) Figure 24.Metal 3D printed rocket engine, Relativity 2019.

Figure 25.Relativity robots,the most advanced large scale metal 3d printing in the world.

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Figure 26. 3D printed rocket with its engine,ready to fly , in less than 2 months.

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2.4.2 Trajectories in machine learning in digital design-fabrication According to Hebron (2016), in the last few years, a series of technical developments in architecture and artificial intelligence, and rapid progress in computing hardware, have created a renewed optimism for machine learning. As a result, new platforms and software that use machine-learning algorithms have emerged. For instance, Autodesk Dreamcatcher is an experimental design platform with focused research probes into generative design systems (Autodeskresearch.com, 2019). It is a generative design system (Figure 27) that enables designers to define their design problem by setting goals and constraints and derive alternative designing solutions within certain limits (Autodeskresearch.com, 2019).

Topology optimisation algorithms generate geometries according to specific given criteria and additive manufacturing is employed in fabrication (Matejka et al., 2018). However, this platform is at a nascent stage and not in wide use with designers yet.

Also, last year’s many design projects such as the ‘Bridge too far’ (Figure 30) were implemented using machine learning at design and fabrication stages.

Robotic arms were used in this project in metal binding. A method called adaptive fabrication was used.

In it, sensors’ feedback re-defined the movements of the robots (Figure 28) for bending metal sheets with great accuracy, resolution, precision and speed (Tamke, Nicholas and Zwierzycki, 2018). ML was applied to manage the forming tolerances (Figure 29) by creating, adapting and improving fabrication instructions in real time (Tamke, Nicholas and Zwierzycki, 2018). Even though, subtractive manufacturing techniques were employed in this project, its methodology of design and fabrication can be applied on a large scale and the knowledge about metalworking and sheet binding gained from the project can be used in metal AM.

Figure 27.Autodesk generative design using machine

Figure 28.Robot bind metal sheets using instructions which

learning algorithms.

are adapted through machine learning.

Figure 29.Metal sheets after robotic fabrication.

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Figure 30.Bridge too far project by Complex Modelling, 2016.

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2.4.3 Trajectories in open source communities Nowadays, open digital fabrication is used in innovative projects such as the ‘fab lab house’ in Barcelona (Figure 32), which was designed by Barcelona fab lab and IAAC and constructed using CNC machines (Figure 31). Its residents can customise it adapt it to the environmental conditions (Gershenfeld (2012).

Its energy footprint is zero and it proves that open source communities can make innovations on large scale (Fablabhouse.com, 2019).

Figure 31.Fab lab house, fabricated using CNC wood milling.

Figure 32.Fab lab house,participatory design and fabrication by the community of Fab labs,IAAC and Center for Bits and Atoms at MIT, Barcelona 2010.

As regards open source communities, currently more than 20 such online communities with more than 880k users, ranging from beginners to experts, are working on 3d printing (Lansard ,2019). Furthermore, according to Rayna, Striukova and Darlington (2015), the online 3d printing communities empower users to innovate and to enhance co-creation. After studying 22 of the most famous open source 3d printing communities (Figure 33), they have argued that inside these environments co-creation can happen in the stages of design, manufacturing, and distribution. They have categorised such platforms in the following groups: i) Printing, ii) Printing services, iii) Design marketplaces, and iv) crowdsourcing platforms.

All these platforms motivate their users to be innovative by co-designing or co-manufacturing or co-distributing. They all are working at prototyping scale.

The platforms are popular because they use desktop 3d printers. Their contribution to innovation can be observed in hardware, e.g., open source 3d printers, software, e.g., slicing programmes, and in the democratization of digital design. Consequently, it can be argued that the creation of online communities for large scale AM can empower the development of the hardware and software needed for bringing AM on an architectural scale by challenging not only the making processes but also how digital design is created.

Figure 33.Categorizing of 22 online 3d printing platforms, by Rayna,Striukova and Darlington (2015).

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Figure 34._

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03| 3Dprint.AI

An open source, machine-learning framework for large-scale AM.

3.1 General description After the examination of the trajectories in AM, in machine learning for digital design-fabrication and in open source communities, 3Dprint.AI is introduced as a conceptual framework using robotic arms for printing, which technically has the potential for practical implementation. The idea of 3Dprint.

AI was presented in the Autodesk University Conference in June 2019 and the poll of the lecture audience presented below was the feedback for the thesis arguments.

As an open source framework, 3Dprint.AI aims to connect labs, institutions, companies, start-ups and even single, verified users. The worldwide existence of labs engaged in experimenting with large scale AM, in universities and industries felt the lack of a framework focused on this technology as a hindrance in converting 3Dprint.AI into a promising and technically feasible technology.

3Dprint.AI works in two modes – the fully automated mode using machine learning algorithms and the manual one (Figure 35). These modes are incorporated into the following steps: i) recording and object recognition process while printing, ii) adaptive feedback process, iii) documentation of printing settings, and iv) saving-distributing data either by google sheets-manual mode or blockchain network-automatic mode. After saving, data can be processed for improving the settings and making predictions either by machine learning algorithms or manually through a critical study of them. Manual

mode can be used at the beginning of the community when there is a small amount of data and the appropriate algorithms have not been written and tested. Like most of the open source communities, 3Dprint.AI is flexible enough to enable addition or removal of steps as necessary for 3Dprint’s future implementation.

As described above, its real innovation is in the identification of printing errors in real time and prediction of the future results while all the information is available to the community. Members can contribute data to the community while they can also test the predictions made by algorithms. In this manner, after many iterations, it would be possible for the community to develop a software for spatial extrusion with robotic arms, fill the existing gap in this field, and make spatial extrusion more accessible by users.

The tools needed for the framework is a robotic arm, an extruder, and a computer. In the beginning, the robotic arm can be a 6-axis robot. The extruder can be made by the community for its use to be shared by all the members. to use the same hardware, for avoiding collisions. Every step of the process is important and demands high precision settings and tooling, as every possible error can lead to different conclusions and decisions. Technical, organisational and ethical issues of this framework are discussed below.

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Figure 35. 3Dprint.AI, the framework in manual and automatic mode.

3.2 Technical specification: Steps I) Printing record && object recognition A camera mounted near the extruder of the robot arm is used for recording all the processes at close quarters. In the automated mode, the camera sends all the recordings to a local computer, which uses an object recognition program to compare the printed model with the digital one. The algorithm scans every layer – lattice – of the printing model, comparing it with the photos for specific printing errors. Printing errors such as in wrapping material, over-extruding etch are commonly known to the community and are very easy to find their photos.

The camera can also be used for live streaming of the process to the robot users so that the process can be implemented manually for an experienced user to identify many extruding errors without the use of software.

II) Adaptive feedback process When the local computer recognises a printing error, it searches the database of possible printing errors to find a match for this error and suggest the steps to follow to rectify it. The computer sends feedback to the robot to change its g-code, which can controls most of the printing processes – from the robot movements to temperature controls.

Most of the times printing errors occur due to high speed or temperature errors. These parameters can be changed easily to improve the printing results significantly. Even though this step seems too complicated, it is given effect gradually during printing.

The same process can be implemented manually by changing the g code of the robot either through the official operation software of the robot such as RobotStudio or by stopping the process to change the parameters of g code through the manual control of the robot.

III) Documentation of printing settings, problems and solutions Once the printing is over, the users have to save all the settings of the process such as motor speeds, temperature etch, and the errors that occurred during printing. If they managed to rectify the errors, they must document the actions they performed to

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make the rectification. They also must take photos of their model, especially where errors occurred. If a user cannot fix an error, it would be documented as an unsolved issue and would be advertised to the community. As users contribute solutions, large amounts of data accumulate. For every possible error, every user may contribute a different solution, of which the optimum one may be used. The documentation may be done in an excel-google sheet document, which can be available to all the members of the community. The data sheet for every printing iteration can either be sourced automatically from the printing software or the user may create it manually.

IV) Saving and distributing data to the community After documentation, users can use the blockchain network of the community to distribute their data (Figure 36). Through machine learning, the existing data can be analysed to determine precisely what further tests are needed to make the predictive model more confident (Zelinski, 2018). However, training a machine learning system tends to be a highly computationally intensive process, which is often impractical or even impossible to perform on a single consumer-grade machine (Hebron, 2016).

For this reason, some users of the community would be designated to implement all the computational calculations to provide ready solutions to the users.

As at the beginning of the community, the available data can be shared even via google sheet documents and some experienced users may decide which parameters were causing errors and propose further tests to other members of the community (manual mode).

Figure 36.3Dprint,AI private blockchain network.

3.3 Organization of the community and data 3.3.1 Governance & hierarchy Pearson (2016) showed that both governance and hierarchy are essential in open source communities, whose organisation, though democratic in many ways, does not vary much from traditional software Development Company. In the 3Dprint.

AI framework, the above characteristics tend to be crucial because evaluation and confirmation of the data submitted by the users before adding it to the database is vital for the reliability of the system. Furthermore, governance is necessary for distributing specific roles and responsibilities among the users when organising new projects, solving issues and answering questions asked by the community, and even defeating virus attacks and malware.

Srnicek (2017) has demonstrated that such open source communities, even though they have governance and rules; enable the users to act in unexpected ways. Indeed, Pearson (2016) has criticised that no matter when and how users interact and partici-

Figure 37.Hierarchy of the community.

pate in online communities, it is the hierarchy which makes the communities flexible and facilitated its divisions into many sub-systems as the number of users increases.

According to Pearson (2016), open source communities have a tight vertical hierarchy structure. At the base of the pyramid are the contributors who develop a small part of work, followed by the maintainers who organise the larger parts of the project and evaluate the work by the contributors, and at the top are the project leaders who collect and assemble all the different parts of the projects (Pearson, 2016).

At least one maintainer will be required to initiate 3Dprint.AI. The maintainer will begin by inviting more users, and as the community expands, the maintainer would become a project leader while other users become maintainers and contributors.

Every new users would be a contributor and deliver work to the maintainer. The user must prove technical skills, willingness and responsibility to the community to become a maintainer (Figure 35).

Project leaders would be responsible for 1) the evalu-

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ation of their contributors, 2) strategic decision-making about the orientation of the future research of their group. For example, they will decide which printing materials they will test, which extrusion method etch. The main roles of maintainers would be: i) to evaluate the slicing settings that the contributors send and submit them to the network, ii) to advertise to the community new printing problems needing solutions according to their leader’s plan, iii) to divide work of conducting specific tests among the contributors. 3Dprint.AI contributors will i) run printing tests and record slicing settings, ii) discuss with other contributors or maintainers the issues that they face while printing, iii) feed the community with feasible creative ideas about various upgrades to hardware and software. In 3Dprint.AI community, reputation would be gained through contribution, and leadership through both contribution and experience (Pearson, 2016).

3.3.2 Data distribution via block-chain network According to Rosic (2019), centralised datasets used to be easy targets for hackers who wanted to either steal valuable information or just corrupt the system.

Laurence (2017) noted that blockchain networks tend to be impenetrable to attacks and sensitive user information could be protected with cryptographic algorithms.

In the 3Dprint.AI framework, a private blockchain network is used. Indeed, every data sheet sent by the contributors and evaluated by maintainers would be stored and available to everyone inside the community to see the data. However, no one would be able to change the data once it is stored in the blockchain.

As a result, machine-learning algorithms developed by the community may be used by the community to run multiple iterations and make predictions. One or more contributors can then test the predictions and if proved right, the maintainers may add them to the blockchain network. Consequently, the data added to the blockchain may be relied on as valid, safe and used by the community.

The data in the blockchain, for better organisation and easier access would be classified into three distinct groups (Figure 36), the first, the documentation of the printing settings and the errors occurring

while extruding. The second, predictions made by machine learning algorithms. The third, the predictions of experienced users implemented manually.

All groups would be advertised to the community and every group will be categorised into subgroups according to the printing error they refer to.

3.3.3 Categorization of data The categorisation of the data within the above three groups would be based on some important printing parameters such as speed, the complexity of geometry, printing temperature, material, calibration, and multiple errors. Machine learning algorithms would be used only with the information in the first two groups as the third group contains only advice from shared by the users. The above categorisation would help better organisation of data to make it easily comprehensible to the learning system (Hebron, 2016). While 3Dprint.AI grows more categories may be added and some others deleted or excluded from use due to obsolescence.

Another important use of categories of data is in analytics, which can reveal trends, relationships between errors or even make predictions to define the future direction of research for the community.

Making groups and subgroups will assist in making better distribution of human resource for achieving specific goals and tasks of the community. Data analytics can illustrate printing errors and their interrelation to facilitate manual and algorithmic predictions.

Figure 38.Categorization of data of the community.

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Problems and challenges

3.4.1. Technical I. Proper dataset

III.Proper documentation

The main problem with 3Dprint.AI is the use of the training data. Hebron (2016) averred that the quality of a given dataset relates to the characteristics of completeness, accuracy, consistency and timeliness.

Indeed, datasets, which are not complete, accurate, consistent and contemporary or recent within a certain time framework may lead to inappropriate decisions because of a large amounts of outdated information being proceed. In AM, often there are more than one reason causing for a problem and it is very challenging even for experienced users to identify all of them. As a result, instead of solving one issue, the datasets could create more, causing more failures in the printing process. Hence, Hebron (2016) noted that an unreliable dataset is worse than no dataset, as computational power is wasted. However, this problem can be minimised by several iterations of the error till the best solution is found and documented in the online database after careful evaluation of the data provided by the contributors.

The proper documentation of all settings is a vital concern because it can affect the predictions of machine learning algorithms. The manual documentation through excel files should be validated in order to be useful to the community. However, at the later stages of development of the community, the documentation, at least the part regarding the printing parameters can be automatically exported to a maintainer of the community from the printing software. To avoid confusion and errors it is necessary that the instructions from the maintainers to the contributors are focused. Indeed, contributors can change printing parameters inside a specific range and not test random changes. Also, the same tests

II. Change of g-code

IV. Same robot & extruder

The changing of the g-code of an industrial robot while it is in operation can be very challenging because any mistake can greatly damage its mechanism. More specifically, every single movement of a 6-axis robotic arm consists of many small sub-movements along its every axis. To avoid such damage and further failures in its environment, every change in these movements should be within the safe axis limits of the robot. However, the existing software for robots such as RobotStudio or even the manual controllers of the current robots are equipped for such functions. At the same time, the parameters of the extruder such as temperature and stepper motor speed are independent of the robot’s parameters such as robot speed, which make the making of the changes easy and safe. Thus, at the beginning of the community, the goal of the framework may be set as maintaining the robot at a steady speed while all the other independent parameters of the extruder may be changed to achieve optimum results with minimum risk.

It is possible that the makers in the community may use different robots, build different extruders and use different slicing software, which would make the provision a universal solution would be very challenging or impossible. In this step, many errors may occur since the difference in the hardware can mislead the makers. To avoid these problems, 3Dprint.

AI must initially specify one model of a robot, one extruder, and one specific extruding material so that safe solutions can be proposed to the users. Later, when the community is ready with a larger amount of data for the use in its algorithms, variety of hardware may be added to provide the users a wider choice.

may be implemented by different contributors for checking the parameters submitted to the maintainers. The comparison of printing outcomes with the same parameters produced by different robots can also be used for testing the reliability of the contributor’s hardware.

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V. Environmental conditions while printing According to Leonidou (2019), one of the most important factors regarding printing quality and consistency is the environmental conditions in which the printer operates, that is to say, users with the same machine, extruder and slicing settings can face different failures –ex. bad adhesion; if they operate the equipment under different environmental conditions such as different temperatures. This issue can be solved through extruding under almost the same environmental conditions controlled inside an enclosure or room and the specifics of the environment to the documentation.

VI. Complexity of the process The complexity of the computationally intensive process remains a crucial issue in the framework. In desktop 3d printing, where the extruding plate is most of the times up to 30cm, almost all the printing

problems such material collision, layer separation etch can be solved through proper slicing of the 3d model and excellent machining. However, Leonidou (2019) demonstrated that in large scale extrusions various other parameters such environmental conditions tend to affect much the printing outcome.

At the same time, on large scales, the cost, time and energy consumed are much more important. However, although camera recording and object recognition while printing is computationally intensive, they provide the advantage of continuous supervision over the process. However, in geometries which are simple or easy to extrude, users can just not use camera recording and adaptive feedback process but merely document the setting- errors that occur in order to produce training data for machine learning algorithms developed by the community.

3.4.2 Ethical Despite the technical challenges, there are many ethical issues that emerge from the operation of 3Dprint.AI community. Most important questions are critically discussed below and two polls are presented (Figure 40). More specifically, after an issue with the Autodesk app during the poll in the Conference, only 10 answers out of 80 were recorded, making the result not reliable. For this reason, a second poll with Bartlett students was also held and received 70 answers. Even though both polls cannot be totally objective due to limited number of participants, they can illustrate the thoughts and arguments of people who can join 3Dprint.AI community in the future.

As an overall trend, both polls illustrate the positive feedback towards 3Dprint.AI community with yes or maybe yes to be the main answers.However, a percent almost 10 % of feedback is maybe no or no.

These arguments are taken into consideration in the discussions below.

Figure 39.Robot

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Figure 40.Polls answers.Generally, answers are supportive towards the idea of 3Dprint.AI community.

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I. Authorship and ownership issues With the development of the digital culture, such buzzwords as ‘collaboration’, ‘open source’, and ‘open in-novation’ have gained currency (Picon 2016). These buzzwords imply such fundamental issues as authorship. According to Rui (2016), the fundamental requirement for claiming authorship is originality. For an authored work to receive rights of property through copyright it should be original, otherwise it would merely be a copy of some other original work (Rui,2016). However, in a framework such as open 3Dprint.AI, it is unclear who has the authorship of data and who has the authority to use that data. In other words, ownership is un-certain and therefore is becoming a more and more central question (Fok and Picon, 2016).

Rui (2016) noted that the basis of all copyright law is the idea that authorship produces a proprietary right over the authored work. Rui has also criticised the remarkable idea of copyright that only the creator of a work owns the exclusive right for its duplication.

Furthermore, he has demonstrated that even literary critics such as Roland Barthes and Harold Bloom have powerfully argued that it is impossible to assert a claim of full originali-ty of any creative work. Nevertheless, in the 21st century, many types of ownership over collaborative-creative work have become possible using digital technologies and humans are just beginning to foresee the conse-quences of this trend (Picon, 2016).

Consequently, in open 3Dprint.AI framework, when many users contribute to the creation of training dataset, in order to avoid legal disputes and arguments, all users have to accept the pre-defined terms and conditions. Indeed, they have to agree about the authority and use of the dataset; the full authorship of which can be reposed, since the beginning, with a university or a research institute such as Bartlett. However, according to Pearson (2016), when the community has gained popularity, it can be converted into an organisation with a specific legal structure which devolves all the rights and obligations upon its members.

II.Access restrictions According to Gershenfeld (2012), in digital fabrication there is always the threat of theft of intellectual

property when makers have the right to replicate designs. Anderson (2012) has argued that the problem of copies in open source environments could be creative because when it is happening, it is a sign of success, as what starts from copying may become a real innovation after improvements. However, Gershenfeld (2012) has clarified that only some designs should be shared with online communities while others, including patents, should be protected.

Another important threat arising from free access in AM tools is the production of guns (Gershenfeld, 2012). Indeed, the democratisation of design and fabrication raises vital questions regarding the free access to all to such tools. More specifically, humans tend to think and decide about technology in their individual ways. Therefore, when the technology is artificial intelligence, applied to large scale AM, it is axiomatic that access to it should be restricted only to verified users for the protection of public safety.

Consequently, 3Dprint.AI can start operating and allow access to its dataset to universities, research institutes, industries, corporations, start-ups, fab labs and other selected and verified users. It cannot be an open source framework data that is open to everyone because all the possible negative consequences of its use for individuals and society have to be avoided.

III. Trust to the system According to Schneier (2019), blockchain has failed to achieve adoption because businesses who are always protective of their sensitive data do not trust it.

On the other hand, Anderson (2012) has argued that individuals and companies like Google are participating in open source platforms, sharing their data and trusting these communities because they aspire to meet more people, and accelerate the innovation process to a speed far greater than can be achieved by conventional development.

Furthermore, Schneier (2019) has criticised that, in blockchain networks, trust is shifting from humans to technology and if it is hacked, there is no recourse for the users who lose all their data. However, Laurence (2017) has shown that the cost of high computational power needed for an attack on a decentralised blockchain network defeats the purpose of

hacking it. In fact, at the time of writing this – 2019 – a whole blockchain network has never been hacked successfully.

Moreover, Schneier (2019) noted that no matter how reliable a technological system – blockchain – is, there are always dangers and threats, especially when it is scaled up. For this reason, many companies are using only the blockchain that they have created and control. On the contrary, Laurence (2017) noted that blockchain developers have invented tools and subsystems which afford extra safety to data when the network is scaling up. Laurence argued that these safety systems have made existing blockchain networks even more difficult to hack or interrupt, particularly when these networks are private.

Therefore, relying on the above experts’ opinions and poll results, the 3Dprint.

AI framework would use a private blockchain network to secure its data against hackers while allowing access to community members. In this way, even companies and individuals whose data are sensitive could participate in the community with assurance that only members have access to the data and hackers cannot steal it.

IV. Participation of the community Anderson (2012) gave numerous reasons for individuals and organisations to participate in open source communities, such as gaining new skills and reputation through them. On the other hand, Carpo (2017) argued that even though there many open source environments, mass collaboration has not happened in design as yet because design professionals have rejected this potential and favoured protecting their privileges. However, Anderson (2012) described the maker movement as a trend, which will evolve in the coming years and engage more people in open source environments.

In the 3Dprint.AI framework, the participation of the community is a crucial issue and the different groups of people who would be expected to engage should be examined separately for their specific characteris-

tics. In Anderson’s opinion (2012), it may be assumed that the interest of both makers and companies to advance large scale AM, the significant number of labs worldwide and the current open source trend ;together can create the momentum by which at least some makers or labs would be motivated to participate in 3Dprint.AI community.

However, Innaccessibility of 3D.print.AI for the following reasons is becoming a major argument. Firstly, it is both difficult and time consuming for people to engage with robots for large-scale AM. Secondly, someone has to teach the prospective members the basic skills and enable to further develop those skills since most of the users may be unfamiliar with industrial robots.

For these reasons, awards and payments, either ethical or financial, could motivate makers who are highly skilled, to share their knowledge with other members of the community and build mutual trust with them. The hierarchy of users can contribute towards this goal as maintainers who could distribute tasks, teach the contributors to do the tasks and supervise their work. Also, the high-quality skills that members may gain in the community can attract users to join.

Companies and start-ups can also join as they can quickly gain knowledge, they can meet new people even develop some of their projects in the community. The private blockchain network can guarantee the security of their data.Companies would continue to participate to benefit from the freely available and growing set of hardware and software. They can use the it to solve their printing issues, for which, otherwise, they have to pay.

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3.4.3. Economic efficiency of the community According to Pearson (2016), when open source communities amass large memberships, wish to protect their property and to survive economically convert into organisations like Linux. By this way, they can receive money from donors while additive advisory committees ensure that the community stays vibrant. Pearson (2016) has found that advisory committees guide communities on the technical, social and political issues and examine these issues to prevent them from becoming problems. Kumar ( 2015) has shown that open source communities earn funds from companies that are interested in their research or by providing support to companies outside the community or by selling their products or from advertisers on their website or apps. Pearson (2016) observed that such organizations in the USA enjoy low tax rates to ensure their existence.

As mentioned before, 3Dprint.AI can start its operation in an institution like the Bartlett and connect labs, companies, start-ups; and users. It can receive money by all the above means. The economic efficiency of this framework is crucial as it develops both hardware-extruder and software about large scale AM. It would need some initial investment to begin its operation, building the extruders and pay some users who spend time in the community for organising projects or teaching the contributors AM skills.

This fund may be raised by crowdfunding or by research grants from institutions or, even, by self-funding by its first users. Finance is a crucial issue which can affect the number of people who will engage.

After some members are enrolled and a reliable dataset is created, members of the community can develop software for slicing 3d models and strategizing toolpaths like the Cura or AIsync, which then can be sold to companies, industries, and labs who do not participate to earn an income for the community.

Kumar (2016) demonstrated that even big companies like Microsoft and Google find it less expensive to pay open source communities like Linux for creating codes than creating the codes themselves. This can happen with the 3Dprint.AI by communicating with companies and industries in the field their future research agenda and recent developments in AM and AI.

Figure 41. General diagramme which illustrates the feedback to the idea of the framework and the framework itself.

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Figure 42. RC1 exruder 2019, designed and developed enabling the operations of 3Dprint.AI framework

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04| Implementation:Manual mode

4.1 The extruder An extruder developed for Research Cluster I, which studies robotic 3d printing applications on an architectural scale, implemented all of the operations of the framework in the manual mode (Figure 42). The device was handmade, and cost around ÂŁ400. All its parts are metal, which enables the extruder to print at high speeds with great precision and zero vibrations. Its bracket and the nozzle are made of aluminium while the heartbreak and gears are stainless steel. An Arduino connects the computer with the pins 2 and 3 of the robot to synchronise the extruder

motor with robot’s moves. The bracket is designed for ABB and Kuka robots. The extruder can be further due to its open plans to students from other clusters.

Even though the implementations of the 3Dprint.AI framework through the extruder is manual, the machine has the specifications for the automatic mode, and automating the extruder is the final goal. Critical analysis and evaluation of the extruder operations are presented below.

Figure 43. Research and development of the extruder towards 3Dprint.AI idea.

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Figure 44. Extruder, final design and components.

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4.2 Implementation Steps I) 4k live streaming with an action camera An action camera mounted near the bracket enables the 4k live streaming function for better supervision of the printing process (Figure 44). In this way, users can record the entire printing process and document the printing errors in real time. In the future, an algorithm can process the video and change the printing settings errors are detected. The video can also be displayed live for the community and for experienced users to give more advice to the contributors during the process of printing.

II) Adaptive feedback: Manual change of robot speed and temperature while printing A PID controller, independent of the robot and the Arduino, adjusts the extrusion temperature (Figure 48). When a printing error occurs, the user can change the temperature in real time and observe the outcomes. The robot speed can also be adjusted manually through the robot controller (Figure 47). First, the robot stops moving and the user types the robot speed manually. Pins 2 and 3 of the robot, which are connected with Arduino, stop the stepper motor of the extruder when the robot has stopped moving. Also, the user can easily change the speed of the extruder through the Arduino code, which is connected to a local PC. In this way, the two most important parameters of spatial printing – speeds of the robot and the extruder, and temperature – can be modified in course of printing (Figure 45).

Figure 45. Adaptive feedback process-manual mode.

Figure 46. Extruder specs (up).

Figure 47. ABB robot controller (middle).

Figure 48. Extruder electronics.The PID controller adjust temperature while printing.(down)

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Figure 49. Layer by layer documentation.Through many iterations reliable big data sets are created enabling ML algorithms of the community to use them in the automatic mode.

Figure 50. Lattice printing documentation. After careful consideration and experimentation with parameters, models are printed with better quality every time.

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Figure 51. Data analytics with trendlines for every parameter of printing.By this way users can identify the relationship between different parameters and do predictions.

III) Documentation in excel-google sheet file The documentation of the printing settings is done manually in a google sheet file. The file contains all the parameters for both, layers by layer (Figure 49) and lattice printing (Figure 50). The user can also add photos of both printing outcomes and digital models to the document for better evaluation of the data by the community. User can also identify manually possible problems and propose solutions using data analytics (Figure 51).

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IV) Data distribution to other teams Finally, the excel datasheet will be given to other students of Bartlett who also are attempting robotic printing. Because the 3Dprint.AI community does not exist yet, the data are distributed through google sheets via link.. After many iterations of spatial extrusion, it was possible to visualise with diagrams the relationship between different parameters and the trends among them. This method is simple, quick and free and it ideal for creating the first reliable datasets (Figure 52 ). At next stages of development, when big amount of data gathered private block chain network would established for the community and machine-learning algorithms would process the data, as it discussed before.

Figure 52. Sharing files and instructions with other students via google sheets.

4.3 Evaluation and further steps The technical implementation of the 3Dprint.AI framework in manual mode is a stepping-stone towards its further automation. Indeed, the DIY extruder proved that high-quality hardware could be developed and shared inside the community at low cost. Live streaming and adaptive feedback functions amplify the effectiveness and reliability of the machine as even manual users can achieve better supervision of the process and adjust the parameters in course of printing. The documentation of printing settings and data analytics enhance the accuracy of predictions since they illustrate the relationships among specific parameters and with printing errors.

As regards the data distribution within the community, the fact that both designs and instructions were shared among the students helped stimulate their creativity. Students acquired skills and knowledge about spatial extrusion and that they quickly replicated the extruder thrice and adjusted it to their needs (Figure 53). The quality of spatial extrusion improved day by day due to the sharing of knowledge and experience. Students not only shared their data sheet but also spent time with other teams and help them solve specific issues in hardware and software.

Students took deep interest in spatial printing as they had other students to support them. B-made workshop managers have already expressed their interest to buy some of the extruders because of their high-quality components.

However, at the time of writing, 3Dprint.AI has not achieved the use of machine-learning algorithms for processing the data and making predictions.

The manual implementation, even though it solved many of the framework’s technical problems, it was not enough for bringing AM on an architectural scale. Many of the framework’s technical steps such as blockchain network have to be developed and tested.

The community has to grow and raise its popularity outside the Bartlett to bring more data sets, more ideas and experience. In this way, it would be possible for the community to develop software for strategizing robot toolpaths and use optimised settings for minimum printing errors. The software should also document automatically the slicing settings and printing errors and distribute them to the community. Finally, the software can be sold to companies and users outside of the community and support can be provided as a service to earn an income.

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Figure 53. Replications of the extruder 3 times,from students of Bartlett (up left to up right).

Figure 54. Details of the RC1 extruder while printing (up left and up right).

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05| Conclusions 5.1 Contributions Through this thesis, an open source machine-learning framework for large-scale additive manufacturing was introduced. It brings together the DIY culture of open source communities, the positive prospects of artificial intelligence, and the use of industrial robots in fabrication. The thesis proposes 3Dprint.AI as a conceptual and technical framework, which has the potential to raise AM to large scale.

The propose 3Dprint.AI can be flexibly implemented in two modes, the manual and the automatic.

It consists of the following steps i)video recording and object recognition while printing, ii) adaptive feedback process, iii) documentation of printing settings and results, and iv) distribution of those settings via google sheet files-manual mode-, or an online distributed network to enable the members to use the machine learning algorithms and make predictions-automatic mode-. The 3Dprint.AI community is organised with flexible vertical hierarchy that permits the addition or removal of groups and subgroups according to the availability of users and project requirements. This hierarchy is divided into i) the contributors, ii) the maintainers, and iii) the project leaders. Various technical and ethical challenges have been taken into consideration as well the organisation of the data, protection of its knowledge; and plans for the economic efficiency of the community have beenpresented.

Successfully implementation of 3Dprint.AI in manual mode was achieved through the development

of an extruder. The quick replication of the extruder thrice by the students of Bartlett and the advancement of AM techniques in a short time exemplified the power of open innovation in an environment of a community. In this way, 3Dprint.AI introduced a pedagogical contribution through the exchange of experience and knowledge between users quickly and without cost. However, the automatic mode using machine learning algorithms and blockchain distribution network is yet to be implemented.

The novel 3Dprint.AI framework takes advantage of human creativity and algorithmic innovation for overcoming the existing limitations of AM applications and raising it to architectural scale. It aspires to interact, inform and empower design not only as a tool for making things but also as method for studying, developing and fabricating forms and structures unknown to humanity before.

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5.2 Concluding remarks Overall, motivated by the existing limitations of ro-

ship and ownership inside the community, access

botic 3d printing, this thesis challenged the current state of the industry by proposing an open source machine learning framework for large scale AM. After contextualising the framework historically and socially in the period of post-digital era when open innovation and computational power are continuously affecting design and making, the thesis debated the main points from various technical, ethical and historical points of view. It studied, criticised and evaluated the trajectories in large scale AM, machine learning applications digital design-fabrication, and in 3d printing platforms.

restrictions, trust in the private blockchain network, and participation of users in the community. These arguments were strengthened by presenting different approached and by criticising two polls, one from Barltett students and the other from Autodesk University Conference where the idea of the this thesis was presented and discussed in detail. Opportunities for economic efficiency of the community were also studied.

After identifying the main drawbacks and issues of the above trajectories, it proposed 3Dprint.AI as a conceptual and technical framework, which operates flexibly in two modes, the manual and the automatic. Also, it described the steps of the framework, which are object recognition while printing, adaptive feedback to the robot, and documentation of the settings and distribution of data through google sheet files-manual mode- or a private blockchain network to enable the use of machine-learning algorithms in making predictions-automatic mode-.

It also presented the organisation of a community with flexible vertical hierarchy of contributors, maintainers and project leaders; organisation of data for better division of work, protection of knowledge, and evaluation of users. The thesis illustrated not only the technical challenges of the framework but also the emerging ethical questions about author-

Further, 3Dprint.AI was implemented successfully in manual mode through the development of a high-quality extruder. The extruder was made for all other students of research cluster I and was replicated thrice by other students of Bartlett who learnt about spatial extrusion, and by exchanging data, they managed to quickly correct the extrusion errors and develop technical skills. This shows that the framework contributes not only technically but also pedagogically.

However, the automatic mode of the framework has not yet been implemented. The creation of the community inside and outside of the Bartlett as well as the development of software for large scale AM to be marketed to companies and users outside the community to earn and income for the community are some of the future plans. Even though many years of studies will be required for bringing AM to architectural scale efficiently, 3Dprint.AI research is well underway.

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