Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. It implies that the proposed data-driven framework can lead to similar manufacturing decisions with reduced costs and time.

翻译：制造具有特定性能或性能组合的先进材料与产品通常具有明确需求。为实现这一目标，关键在于确定能够产生理想性能组合的最优配方或工艺条件。多数情况下，需要开展足量实验以生成帕累托前沿。然而，制造实验通常成本高昂，且即便单次实验也可能耗时甚巨。因此，确定数据采集的最优位置以获取对工艺过程最全面的理解至关重要。序贯学习是一种极具前景的方法，它能从持续进行的实验中主动学习，迭代更新底层优化程序，并动态调整数据采集过程。本文提出了一种新颖的数据驱动贝叶斯优化框架，该框架利用序贯学习高效优化具有多个冲突目标的复杂系统。此外，本文提出了一种用于评估多目标数据驱动优化方法的新指标，该指标兼顾帕累托前沿的质量与生成该前沿所需的数据量。所提出的框架在数据采集成本高昂且资源密集的实际应用场景中尤为有益。为验证所提算法与指标的有效性，我们在制造数据集上进行了评估。结果表明，该算法能在处理显著更少数据的情况下实现真实帕累托前沿。这意味着该数据驱动框架能够以更低的成本与时间达成相似的制造决策。