Robotic Training for the Integration of Material Performances in Timber Manufacturing

The research focuses on testing a series of material-sensitive robotic training methods that flexibly extend the range of subtractive manufacturing processes available to designers based on the integration of manufacturing knowledge at an early design stage. In current design practices, the lack of feedback information between the different steps of linear design workflows forces designers to engage with only a limited range of standard materials and manufacturing techniques, leading to wasteful and inefficient solutions. With a specific focus on timber subtractive manufacturing, the work presented in this thesis addresses the main issue hindering the utilisation of non-standard tools and heterogeneous materials in design processes which is the significant deviation between what is prescribed in the digital design environment and the respective fabrication outcome. To begin, it has been demonstrated the extent to which the heterogeneous properties of timber affect the outcome of the robotic carving process beyond the acceptable tolerance thresholds for design purposes. Resting on this premise, the devised strategy to address such a material variance involved capturing, transferring, augmenting and integrating manufacturing knowledge through the collection of real- world fabrication data, both by human experts and robotic sessions, and training of machine learning models (i.e. Artificial Neural Networks) to achieve an accurate simulation of the robotic manufacturing task informed by specific sets of tools affordances and material behaviours. The results of the training process have demonstrated that it is possible to accurately simulate the carving process to a degree sufficient for design applications, anticipating the influence of material and tool properties on the carved geometry. The collaborations with the industry partners of the project, ROK Architects (Zürich) and BIG (Copenhagen), provided the opportunity to assess the different practical uses and related implications of the tools in a real-world scenario following an open-ended and explorative approach based on several iterations of the full design-to-production cycle. The findings have shown that the devised strategy supports decision-making procedures at an early stage of the design process and enables the exploration of novel, previously unavailable, solutions informed by material and tool affordances

Development of a design feature database to support design for additive manufacturing (DfAM)

This research introduces a method to aid the design of products or parts to be made using Additive Manufacturing (AM), particularly the laser sintering (LS) system. The research began with a literature review that encompassed the subjects of design and AM and through this the need for an assistive design approach for AM was identified. Undertaking the literature review also confirmed that little has been done in the area of supporting the design of AM parts or products. Preliminary investigations were conducted to identify the design factors to consider for AM. Two preliminary investigations were conducted, the first investigation was conducted to identify the reasons for designing for AM, the need for a design support tool for AM and current challenges of student industrial designers designing parts or products for AM, and also to identify the type of design support they required. Further investigation were conducted to examine how AM products are developed by professional industrial designers and to understand their design processes and procedures. The study has identified specific AM enabled design features that the designers have been able to create within their case study products. Detailed observation of the case study products and parts reveals a number of features that are only economical or possible to produce with AM. A taxonomy of AM enabled design features was developed as a precursor for the development of a computer based design tool. The AM enabled design features was defined as a features that would be uneconomical or very expensive to be produced with conventional methods. The taxonomy has four top-level taxons based on four main reasons for using AM, namely user fit requirements, improved product functionality requirements, parts consolidation requirements and improvement of aesthetics or form requirements. Each of these requirements was expanded further into thirteen sub categories of applications that contained 106 examples of design features that are only possible to manufacture using AM technology. The collected and grouped design features were presented in a form of a database as a method to aid product design of parts or products for AM. A series of user trials were conducted that showed the database enabled industrial designers to visualise and gather design feature information that could be incorporated into their own design work. Finally, conclusions are drawn and suggestions for future work are listed. In summary, it can be concluded that this research project has been a success, having addressed all of the objectives that were identified at its outset. From the user trial results, it is clear to see that the proposed tool would be an effective tool to support product design for AM, particularly from an educational perspective. The tool was found to be beneficial to student designers to take advantage of the design freedom offered by AM in order to produce improved product design. As AM becomes more widely used, it is anticipated that new design features will emerge that could be included in future versions of the database so that it will remain a rich source of inspirational information for tomorrow s industrial designers

A design performance driven learning framework for conceptual design knowledge : methodology development and applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2008.Pages 169 and 170 blank.Includes bibliographical references (p. 165-167).This thesis develops a learning framework for automation of acquisition of bridge conceptual design knowledge. The thesis proposes a new learning methodology explicitly aimed at capturing quality design aspects to help engineer gain insight into good design. The research uses the National Bridge Inventory (NBI) data, which contains more than 600,000 bridges. The physical condition ratings are used as proxies for design quality. In this data the relationships between physical condition ratings and bridge design elements are not well-known. The simultaneous equation model (SEM) technique is employed to model the physical condition ratings. SEM has the advantage over existing methods of state transition probability estimation in that no a-priori subjective conditional grouping is required. The resulting model yields the marginal effects of design variables on condition ratings, which is easy for engineers to interpret. The analysis results reveal that design features available in the NBI database alone do not adequately explain the resulting condition ratings. Using the identified performance model, COBWEB, an incremental clustering algorithm, is employed to learn mappings from design specification to configuration space. However, the COBWEB branching strategy focuses on probabilistic predictability of feature values. The learned knowledge therefore represents not clusters of good design aspects but rather clusters of local similarity. A modification to the existing strategy is proposed. A set of experiments has been conducted to compare the original and the modified COBWEB. Finally, the thesis provides a detailed discussion of issues related to the quality of the NBI database and proposes strategies for improved analysis of the NBI bridge data.by Sakda Chaiworawitkul.Ph.D

Cognition and the Engineering Design Requirement

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN058277 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Development of a design feature database to support design for additive manufacturing (DfAM)

This research introduces a method to aid the design of products or parts to be made using Additive Manufacturing (AM), particularly the laser sintering (LS) system. The research began with a literature review that encompassed the subjects of design and AM and through this the need for an assistive design approach for AM was identified. Undertaking the literature review also confirmed that little has been done in the area of supporting the design of AM parts or products. Preliminary investigations were conducted to identify the design factors to consider for AM. Two preliminary investigations were conducted, the first investigation was conducted to identify the reasons for designing for AM, the need for a design support tool for AM and current challenges of student industrial designers designing parts or products for AM, and also to identify the type of design support they required. Further investigation were conducted to examine how AM products are developed by professional industrial designers and to understand their design processes and procedures. The study has identified specific AM enabled design features that the designers have been able to create within their case study products. Detailed observation of the case study products and parts reveals a number of features that are only economical or possible to produce with AM. A taxonomy of AM enabled design features was developed as a precursor for the development of a computer based design tool. The AM enabled design features was defined as a features that would be uneconomical or very expensive to be produced with conventional methods. The taxonomy has four top-level taxons based on four main reasons for using AM, namely user fit requirements, improved product functionality requirements, parts consolidation requirements and improvement of aesthetics or form requirements. Each of these requirements was expanded further into thirteen sub categories of applications that contained 106 examples of design features that are only possible to manufacture using AM technology. The collected and grouped design features were presented in a form of a database as a method to aid product design of parts or products for AM. A series of user trials were conducted that showed the database enabled industrial designers to visualise and gather design feature information that could be incorporated into their own design work. Finally, conclusions are drawn and suggestions for future work are listed. In summary, it can be concluded that this research project has been a success, having addressed all of the objectives that were identified at its outset. From the user trial results, it is clear to see that the proposed tool would be an effective tool to support product design for AM, particularly from an educational perspective. The tool was found to be beneficial to student designers to take advantage of the design freedom offered by AM in order to produce improved product design. As AM becomes more widely used, it is anticipated that new design features will emerge that could be included in future versions of the database so that it will remain a rich source of inspirational information for tomorrow s industrial designers.EThOS - Electronic Theses Online ServiceGBUnited Kingdo