Design exploration is an important step in the engineering design process. This involves the search for design/s that meet the specified design criteria and accomplishes the predefined objective/s. In recent years, machine learning-based approaches have been widely used in engineering design problems. This paper showcases Artificial Neural Network (ANN) architecture applied to an engineering design problem to explore and identify improved design solutions. The case problem of this study is the design of flexible disc elements used in disc couplings. We are required to improve the design of the disc elements by lowering the mass and stress without lowering the torque transmission and misalignment capability. To accomplish this objective, we employ ANN coupled with genetic algorithm in the design exploration step to identify designs that meet the specified criteria (torque and misalignment) while having minimum mass and stress. The results are comparable to the optimized results obtained from the traditional response surface method. This can have huge advantage when we are evaluating conceptual designs against multiple conflicting requirements.

翻译：设计探索是工程设计过程中的重要环节，涉及搜寻满足指定设计标准并实现预定目标的单组或多组设计方案。近年来，基于机器学习的方法已广泛应用于工程设计问题中。本文展示了一种人工神经网络架构，应用于工程设计问题中以探索并识别改进的设计方案。本研究的案例问题是用于盘式联轴器的柔性盘元件设计。我们需要通过降低质量和应力来改进盘元件的设计，同时不降低扭矩传递和不对中补偿能力。为实现这一目标，我们在设计探索步骤中采用人工神经网络结合遗传算法，以识别满足指定标准（扭矩和不对中）且质量和应力最小的设计方案。结果与传统响应面方法获得的优化结果相当。当我们在多个冲突需求下评估概念设计时，这具有巨大优势。