Multi-objective optimization of adhesive bonding process in constrained and noisy settings
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This research applies machine learning models within a Bayesian optimization framework to efficiently identify Pareto-optimal parameters for adhesive bonding processes, balancing strength, cost, and quality constraints. By emulating objective and constraint functions with limited experimental data, the approach reduces the need for costly and time-consuming lab experiments.