Design Mining: A Microbial Fuel Cell Pilot Study

设计挖掘是利用计算智能技术，通过快速原型技术直接评估物理对象的属性空间，以迭代搜索和建模，以满足给定的目标。它可以在没有正式模型或复杂模拟的情况下开发新材料和工艺，同时利用计算和人类设计方法的创造力。传统的工程设计过程和数据挖掘过程有许多相似之处，拟议的项目将寻求利用这一事实，并在设计中嵌入数据挖掘。模型，使假设测试的对象设计空间的特点是创建整个。样本-模型-搜索-样本循环创建了一种敏捷/灵活的方法，即主要是测试驱动，使生产者和用户能够持续进行原型设计考虑和标准细化。并行/子设计方案也将进行探讨，考虑到原型和数据/模型同步的程度，在并行任务的效用的方法。特别是，机器学习技术将用于迭代搜索和建模由微生物燃料电池的性能指标所告知的对象设计空间，微生物燃料电池的电极是使用3D打印制造的，既作为单独的单元，也作为级联中的集体。

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.