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重要的一步。这包括寻找满足指定设计标准和达成预定义目标的设计方案。近年来,机器学习方法已经广泛应用于工程设计问题。本文展示了应用人工神经网络(ANN)结构解决工程设计问题,以探索和识别改进的设计解决方案。本研究的案例问题是设计用于盘式联轴器中的柔性盘元件。我们需要通过降低质量和应力而不降低扭矩传递和误差补偿能力,改善盘片的设计。为了实现这一目标,我们在设计探索步骤中采用ANN结合遗传算法,识别满足指定标准(扭矩和误差补偿)的设计方案,同时具有最小化质量和应力。结果与传统响应面方法优化结果相当。这对于针对多个冲突要求评估概念设计时具有巨大优势。