Design Mining: A Microbial Fuel Cell Pilot Study

设计挖掘是利用计算智能技术，对直接通过快速原型技术评估的物理对象的属性空间进行迭代搜索和建模，以满足给定的目标。它使新材料和工艺的开发没有正式的模型或复杂的模拟，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，建议的项目将寻求利用这一事实并将数据挖掘嵌入设计中。可以对对象设计空间的特征进行假设测试的模型贯穿始终。样本-模型-搜索-样本循环创建了一种敏捷/灵活的方法，即主要由测试驱动，允许生产者和用户对原型设计进行持续的考虑和标准改进。还将探讨并行/子设计场景，考虑并发任务中原型和数据/模型同步程度对方法效用的影响。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通知的对象设计空间，这些燃料电池的电极使用3D打印制造，无论是作为单个单元还是作为级联的集体。

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.