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Adaptive Robotic Carving
Over the last decades, digital fabrication technologies have become increasingly available, however, manufacturing knowledge is rarely integrated within the established workflows of design practices. Materialisation processes are regarded as the last stage of design to manufacturing workflows where materials are considered as passive receivers of a previously generated ideal form stored in a CAD model. Such linear progression from the design intention to its materialisation determines a lack of feedback between the different stages of the process and forces design practices to engage with only a limited range of standard manufacturing methods and materials. In most cases, the lack of an integrated material understanding leads to a waste of resources determined by a limited exploration of the full range of solutions available and only partial use of the performance capabilities of the material system. In response to the inefficient linearity of current production workflows, the approach proposed in the thesis seeks to encapsulate manufacturing knowledge into a transmissible form and make it available to designers at an early stage of the design process. Focusing on robotic manufacturing with timber, the central challenge addressed in the thesis is the deviation between what is prescribed in the digital environment and the fabricated operation which result too significant to consider processes, such as robotic carving with chisels and gouges, within the range of manufacturing methods available for practical design applications. The research presents a series of methods to capture, transfer, augment and integrate manufacturing knowledge through the collection of real-world fabrication data with different sensor devices and training of machine learning models, such as Artificial Neural Networks, to achieve an accurate simulation of the manufacturing task informed by specific sets of tools affordances and material behaviours. Several fabrication datasets have been recorded with the aim of assessing the validity of the methods to different manufacturing conditions, in terms of different material properties (e.g. grain structure), wood species and carving gouges. The results successfully demonstrate the ability of the trained networks to accurately predict the outcome of carving operations defined in the digital design environment from a series of fabrication parameters. The application of the methods in a design environment has been assessed during the industry secondments with the two partners of the project: ROK (Zurich) and BIG (Copenhagen). Such collaborations provided the opportunity to apply the devised manufacturing strategies into the established workflow of design firms, developing a catalogue of digital explorations and fabricated material evidence demonstrating the potential benefits and limits of the approach. With the integration of manufacturing knowledge at an early design stage, the impact of the research lies in devising a series of robotic training methods that allow to flexibly extend the range of subtractive manufacturing processes available to designers and support decision-making procedures based on an accurate simulation of non-standard operations on timber for the exploration of novel design opportunities based on material affordances.
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