Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. However, the growing scale of simulations demands substantial expert knowledge, computational resources, and time. A key challenge is identifying input parameters that yield optimal results, as iterative simulations are costly and can have a large environmental impact. This paper presents an AI-assisted workflow that reduces expert involvement in parameter optimization through the use of Bayesian optimization. Furthermore, we present an active learning variant of the approach, assisting the expert if desired. A deep learning model provides an initial parameter estimate, from which the optimization cycle iteratively refines the design until a termination condition (e.g.,energy budget or iteration limit) is met. We demonstrate our approach, based on a sheet metal forming process, and show how it enables us to accelerate the exploration of the design space while reducing the need for expert involvement.

翻译：数值模拟通过降低原型制作成本、减少设计迭代次数，并使产品工程师能够更高效地探索设计空间，从而彻底改变了工业设计流程。然而，日益增长的模拟规模需要大量的专业知识、计算资源和时间。一个关键挑战在于识别能够产生最优结果的输入参数，因为迭代模拟成本高昂且可能对环境产生重大影响。本文提出了一种基于贝叶斯优化的人工智能辅助工作流程，该流程减少了参数优化过程中专家的参与。此外，我们提出了该方法的一种主动学习变体，可在需要时为专家提供辅助。深度学习模型提供初始参数估计，优化循环在此基础上迭代改进设计，直至满足终止条件（例如能源预算或迭代限制）。我们以钣金成形工艺为基础演示了该方法，并展示了它如何能够在减少专家参与需求的同时，加速设计空间的探索。