  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.
