🤖 AI Summary
In multi-objective optimization of metal cutting, dynamically changing requirements lead to prohibitively high computational costs associated with repeated finite-element simulations.
Method: This paper proposes a bio-inspired system flexibility framework. It extends NSGA-II by introducing two novel mechanisms—“variable-objective optimization” and “activated/inactivated genotypes”—to enable effective solution transfer across diverse material-specific tasks. A multi-task benchmark is constructed based on an improved Oxley orthogonal cutting model, integrating dynamic evolutionary optimization with configurable genotype encoding.
Contribution/Results: Experiments demonstrate that the proposed method significantly reduces the number of required simulation evaluations for optimizing new tasks, compared to standard NSGA-II with conventional transfer strategies. This validates both the efficacy of cross-task solution reuse and the structural adaptability of the framework. The approach establishes an efficient, transferable evolutionary paradigm for high-fidelity, dynamic optimization of manufacturing processes.
📝 Abstract
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.
