用AI自动优化钣金成形参数，省专家、省时间、降成本。
When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming
- 结合贝叶斯优化与深度学习，自动寻找最优设计参数。
- 相比传统方法，显著减少仿真次数与计算资源消耗。
- 适合制造业研发团队快速迭代产品设计。
数值模拟已彻底改变工业设计流程，降低了原型制作成本、减少了设计迭代次数，并使产品工程师能够更高效地探索设计空间。然而，模拟规模的扩大带来了对专业知识、计算资源和时间的巨大需求。其中关键挑战在于识别能产生最佳结果的输入参数，因为反复仿真是昂贵且可能带来较大环境影响的。本文提出一种基于贝叶斯优化的AI辅助工作流，可大幅减少专家在参数优化中的参与。此外，还引入一种主动学习变体，可在需要时协助专家。通过深度学习模型提供初始参数估计，优化循环持续迭代直至达到终止条件（如能量预算或迭代上限）。我们在钣金成形工艺上验证了该方法，证明其能加速设计空间探索，同时降低对专家经验的依赖。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。