We present a novel computational paradigm for process design in manufacturing processes that incorporates simulation responses to optimize manufacturing process parameters in high-dimensional temporal and spatial design spaces. We developed a differentiable finite element analysis framework using automatic differentiation which allows accurate optimization of challenging process parameters such as time-series laser power. We demonstrate the capability of our proposed method through three illustrative case studies in additive manufacturing for: (i) material and process parameter inference using partial observable data, (ii) controlling time-series thermal behavior, and (iii) stabilizing melt pool depth. This research opens new avenues for high-dimensional manufacturing design using solid mechanics simulation tools such as finite element methods. Our codes are made publicly available for the research community at https://github.com/mojtabamozaffar/differentiable-simulation-am.

翻译：我们为制造工艺的工艺设计提出了一个新型的计算模式,其中包括模拟反应,以优化高维时空空间设计空间的制造工艺参数;我们开发了一个使用自动区分的可区分的有限要素分析框架,以便准确优化具有挑战性的工艺参数,例如时间序列激光功率;我们通过在添加剂制造中的三个说明性案例研究,展示了我们拟议方法的能力,这些研究涉及:(一) 使用部分可观测数据对材料和工艺参数进行推断,(二) 控制时间序列热行为,以及(三) 稳定熔融池深度;这一研究为利用诸如有限元素方法等固态机械模拟工具进行高维制造设计开辟了新的途径;我们的代码在https://github.com/mojtabamozaffar/difficiable-simulation-am上向研究界公开。