Design Mining: A Microbial Fuel Cell Pilot Study

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Design Mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping technology to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation, whilst harnessing the creativity of both computational and human design methods. The traditional engineering design process and the data mining process share many similarities, and the proposed project will seek to exploit this fact and embed data mining within design. Models which enable what-if testing of the characteristics of the object design space are created throughout. A sample-model-search-sample loop creates an agile/flexible approach, ie, primarily test-driven, enabling a continuing process of prototype design consideration and criteria refinement by both producers and users. Parallel/sub-design scenarios will also be explored, considering the effects of the degree of prototype and data/model synchronisation in the concurrent tasks upon the utility of the approach. In particular, machine learning techniques will be used to iteratively search and model the object design space informed by the performance metrics of microbial fuel cells whose electrodes are fabricated using 3D printing, both as individual units and as collectives in cascades.