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).

翻译：粘接接头因其高比强度、设计灵活性、应力集中小、平面力传递、良好的损伤容限及抗疲劳性等优势，在工业领域中的应用日益广泛。然而，确定粘接工艺的最优参数面临诸多挑战：该优化本质上是多目标的（需同时最大化断裂强度和最小化成本），受约束的（工艺过程不得造成材料可见损伤，且应力测试中不得出现与粘附相关的失效），以及不确定的（相同工艺参数多次测试可能产生不同断裂强度）。实验室中的实物物理实验成本高昂。传统进化方法（如遗传算法）因所需评估实验量过大而难以适用。尽管基于贝叶斯优化的算法更适用于求解此类高成本问题，但现有方法鲜少同时考虑多个（含噪声）目标函数与多个约束条件的联合优化。本研究成功应用特定机器学习技术（高斯过程回归），基于有限实验数据对目标函数和约束函数进行仿真建模。将该技术嵌入贝叶斯优化算法后，能够以极高效率（即仅需少量实物实验）检测出帕累托最优工艺参数组合。