  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.
