Optimizing manufacturing process parameters is typically a multi-objective problem with often contradictory objectives such as production quality and production time. If production requirements change, process parameters have to be optimized again. Since optimization usually requires costly simulations based on, for example, the Finite Element method, it is of great interest to have means to reduce the number of evaluations needed for optimization. To this end, we consider optimizing for different production requirements from the viewpoint of a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks, which also relates to dynamic evolutionary optimization. Based on the extended Oxley model for orthogonal metal cutting, we introduce a multi-objective optimization benchmark where different materials define related optimization tasks, and use it to study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimizes solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs, although further work is needed towards making the methods advantageous for real applications.

翻译：优化制造工艺参数通常是一个多目标问题，其中常常涉及相互矛盾的目标，例如生产质量和生产时间。如果生产需求发生变化，则必须重新优化工艺参数。由于优化通常需要基于例如有限元方法进行成本高昂的仿真，因此找到能够减少优化所需评估次数的方法具有重要意义。为此，我们从系统灵活性框架的角度出发，考虑针对不同生产需求的优化问题，该框架使我们能够研究算法将先前优化任务中的解进行迁移的能力，这也与动态进化优化相关。基于正交金属切削的扩展Oxley模型，我们引入了一个多目标优化基准，其中不同的材料定义了相关的优化任务，并利用该基准研究NSGA-II的灵活性。我们通过两种变体对NSGA-II进行扩展：1) 可变目标，同时优化两个任务的解，以获得预期更具适应性的中间源解；2) 活跃-非活跃基因型，容纳可激活或停用的不同可能性。结果表明，使用标准NSGA-II的自适应方法大大减少了达到目标优化所需的评估次数，而所提出的变体进一步降低了自适应成本，尽管仍需进一步研究以使这些方法在实际应用中更具优势。