A Bayesian network classifier for quantifying design and performance flexibility with application to a hierarchical metamaterial design problem
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Design problems in engineering are typically complex, and are therefore decomposed into a hierarchy of smaller, simpler design problems by the design management. It is often the case in a hierarchical design problem that an upstream design team’s achievable performance space becomes the design space for a downstream design team. A Bayesian network classifier is proposed in this research to map and classify a design team’s attainable performance space. The classifier will allow for enhanced collaboration between design teams, letting an upstream design team efficiently identify and share their attainable performance space with a downstream design team. The goal is that design teams can work concurrently, rather than sequentially, thereby reducing lead time and design costs. In converging to a design solution, intelligently narrowing the design space allows for resources to be focused in the most beneficial regions. However, the process of narrowing the design space is non-trivial, as each design team must make performance trade-offs that may unknowingly affect other design teams. The performance space mapping provided by the Bayesian network classifier allows designers to better understand the consequences of narrowing the design space. This knowledge allows design decisions to be made at the system-level, and be propagated down to the subsystem-level, leading to higher quality designs. The proposed methods of mapping the performance space are then applied to a hierarchical, multi-level metamaterial design problem. The design problem explores the possibility of designing and fabricating composite materials that have desirable macro-scale mechanical properties as a result of embedded micro-scale inclusions. The designed metamaterial is found to have stiffness and loss properties that surpass those of conventional composite materials.