Machine Learning-Assisted Dynamic Multi-Objective Optimization for Enhancing SMT Quality and Efficiency

Quality and efficiency objectives in surface mount technology (SMT) production are inherently conflicting, posing a significant challenge for collaborative optimization. Dynamic factors, such as process parameter anomalies and mechanical failures, substantially increase the complexity of SMT production optimization. However, existing research typically addresses efficiency or quality objectives separately. This article integrates automatic optical inspection (AOI) feedback into the surface mounter, introducing closed-loop quality optimization on top of the original efficiency optimization to enhance overall production performance dynamically. A multi-objective mixed integer programming model is first formulated to enable dynamic head workload assignment for small-scale problems. For large-scale instances, offline optimization generates Pareto-optimal solutions that balance SMT quality and efficiency for practical production planning by utilizing an improved MOEA/D algorithm with decision tree-based operator selection. This algorithm integrates component clustering encoding, heuristic decoding, and adaptive operator selection. Online dynamic optimization is subsequently performed in response to AOI inspection feedback. A dynamic head workload adjustment heuristic is utilized to maximize efficiency while ensuring that quality metrics are met. These two parts constitute the machine learning-assisted dynamic multi-objective optimization (ML-DMO) framework. Experimental validation demonstrates that ML-DMO achieves competitive performance against state-of-the-art algorithms in both multi-objective optimization convergence and practical production efficiency, while enabling dynamic workload adjustment in response to real-time quality feedback.