  Adhesive joints are increasingly used in industry for a wide variety of
applications because of their favorable characteristics such as high
strength-to-weight ratio, design flexibility, limited stress concentrations,
planar force transfer, good damage tolerance, and fatigue resistance. Finding
the optimal process parameters for an adhesive bonding process is challenging:
the optimization is inherently multi-objective (aiming to maximize break
strength while minimizing cost), constrained (the process should not result in
any visual damage to the materials, and stress tests should not result in
failures that are adhesion-related), and uncertain (testing the same process
parameters several times may lead to different break strengths). Real-life
physical experiments in the lab are expensive to perform. Traditional
evolutionary approaches (such as genetic algorithms) are then ill-suited to
solve the problem, due to the prohibitive amount of experiments required for
evaluation. Although Bayesian optimization-based algorithms are preferred to
solve such expensive problems, few methods consider the optimization of more
than one (noisy) objective and several constraints at the same time. In this
research, we successfully applied specific machine learning techniques
(Gaussian Process Regression) to emulate the objective and constraint functions
based on a limited amount of experimental data. The techniques are embedded in
a Bayesian optimization algorithm, which succeeds in detecting Pareto-optimal
process settings in a highly efficient way (i.e., requiring a limited number of
physical experiments).
