Common goals of modern production processes are precision and efficiency. Typically, they are conflicting and cannot be optimized at the same time. Multi-objective optimization methods are able to compute a set of good parameters, from which a decision maker can make a choice for practical situations. For complex processes, the use of physical experiments and/or extensive process simulations can be too costly or even unfeasible, so the use of surrogate models based on few simulations is a good alternative. In this work, we present an integrated framework to find optimal process parameters for a laser-based material accumulation process (thermal upsetting) using a combination of meta-heuristic optimization models and finite element simulations. In order to effectively simulate the coupled system of heat equation with solid-liquid phase transitions and melt flow with capillary free surface in three space dimensions for a wide range of process parameters, we introduce a new coupled numerical 3d finite element method. We use a multi-objective optimization method based on surrogate models. Thus, with only few direct simulations necessary, we are able to select Pareto sets of process parameters which can be used to optimize three or six different performance measures.

中文翻译：

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仿真和多目标优化，以提高基于激光的材料累积过程的最终形状和过程效率

现代生产流程的共同目标是精度和效率。通常，它们是冲突的，不能同时进行优化。多目标优化方法能够计算出一组好的参数，决策者可以根据这些参数为实际情况做出选择。对于复杂的过程，使用物理实验和/或广泛的过程模拟可能会过于昂贵，甚至不可行，因此使用基于少量模拟的替代模型是一个很好的选择。在这项工作中，我们提出了一个综合的框架，以结合使用元启发式优化模型和有限元模拟来找到基于激光的材料累积过程（热thermal粗）的最佳过程参数。为了有效地模拟在三个空间维度上具有宽广的过程参数的，具有固液相变和带有毛细自由表面的熔体流动的热方程耦合系统，我们引入了一种新的耦合数值3d有限元方法。我们使用基于代理模型的多目标优化方法。因此，仅需很少的直接模拟，我们便能够选择Pareto工艺参数集，这些参数可用于优化三个或六个不同的性能指标。