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:-
约束条件下稀疏数据情况下的流程设计参数的多目标优化:以粘接为例
翻译摘要:
粘接连接由于其高强度重量比、设计灵活性、限制应力集中、平面转移力、良好的损伤容忍度和抗疲劳性等优点而在工业中越来越广泛地使用。寻找粘接过程的最佳工艺参数具有挑战性:优化本质上是多目标的(旨在在最小化成本的同时最大化断裂强度),受约束的(过程不应导致任何材料的可视化损坏,并且应力测试不应导致粘附相关问题的故障),且存在不确定性(多次测试相同的工艺参数可能导致不同的断裂强度)。在实验室中进行真实的物理实验非常昂贵。传统的进化方法(例如遗传算法)因需要评估的实验数量而不适用于解决该问题。尽管基于贝叶斯优化的算法是解决这种昂贵问题的首选方法,但极少数方法同时考虑优化多个(嘈杂的)目标和多个约束条件。在这项研究中,我们成功应用了特定的机器学习技术(高斯过程回归)来模拟基于有限实验数据的目标和约束函数。这些技术嵌入到一个贝叶斯优化算法中,成功地以高效的方式(即仅需要有限数量的实际实验)检测出帕累托最优流程设置。