A Meta-Optimization Framework Based on Hybrid Neuro-Regression for Quality-Oriented Laser Transmission Welding of PMMA–Metal Joints

İngilizce (OpenAlex)

This study presents an integrated modeling and optimization framework for laser transmission welding (LTW) of transparent polymethyl methacrylate (PMMA) joints using single- and multi-core copper wires as energy absorbers. The highly nonlinear relationships between laser power, welding speed, and spot diameter and the resulting shear force and weld width were modeled using a hybrid neuro-regression strategy combining data-driven learning with physically interpretable analytical formulations. A wide range of candidate mathematical models were systematically evaluated based on training and testing performance, residual behavior, and physical consistency. The results demonstrate that models exhibiting near-perfect training accuracy frequently suffered from severe overfitting and poor generalization, whereas intermediate-complexity formulations provided a more reliable balance between accuracy and robustness. Comparative analysis further showed that multi-core absorbers consistently produced higher shear strength and more uniform weld seams than single-core configurations. The selected robust models were subsequently integrated into a two-level ensemble meta-optimization framework employing Differential Evolution, Nelder–Mead, Random Search, and Simulated Annealing algorithms under multiple design scenarios. The meta-optimization process successfully eliminated model- and algorithm-dependent extreme solutions and identified stable consensus parameter regions. For the multi-core system, an optimal combination of 30 W laser power, 20 mm/s welding speed, and 0.7 mm spot diameter was obtained, achieving improved mechanical performance while remaining within experimentally validated limits. The proposed framework provides a physically grounded and reliable strategy for surrogate-based optimization of nonlinear welding processes.