Multi-objective simulation optimization of the adhesive bonding process of materials
This addresses the problem for automotive companies seeking efficient optimization of bonding processes for lightweight materials, but it is incremental as it applies existing methods to a specific domain.
The researchers tackled the challenge of finding optimal process parameters for adhesive bonding in automotive lightweight materials by applying Bayesian optimization with Gaussian Process and Logistic Regression, successfully guiding experiments to Pareto-optimal settings with few experiments.
Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together. Finding the optimal process parameters for such adhesive bonding process is challenging. In this research, we successfully applied Bayesian optimization using Gaussian Process Regression and Logistic Regression, to efficiently (i.e., requiring few experiments) guide the design of experiments to the Pareto-optimal process parameter settings.