Research on Adaptive Optimization Methods for Multi-Objective Manufacturing Process Parameters in Complex Assembly Processes

High coupling exists among parameters in complex assembly processes, leading to conflicting and drifting objectives (quality, cycle time, and energy). We propose an adaptive multi-objective parameter optimization framework that integrates a tuned digital twin, a set of surrogate models aware of uncertainty, constrained expected hypervolume improvement optimization, and online drift compensation. On three publicly available manufacturing/inspection datasets and a highly simulated assembly twin, our method improves hypervolume by 4.0 to 5.0 points compared to a strong baseline, while simultaneously reducing defect rate and cycle time with fewer evaluations. Paired t-tests show significant improvements (p < 0.001) at the 95% confidence interval. Abstract: This framework is a scalable, general, and data-efficient adaptive tuning scheme for complex assemblies with uncertainty.