Operations Research Meets Machine Learning: Bayesian Optimization for Accelerating the Product Development in Additive Manufacturing and Thermoelectric Materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains challenging due to costly and time-consuming manufacturing processes. These processes often involve tens of variables, resulting in hundreds of thousands of possible experiments, with each experiment carrying significant resource costs. In such high-dimensional domains, traditional Edisonian, trial-and-error approaches become prohibitively expensive and inefficient at identifying optimal experimental conditions. Consequently, there is a critical need to shift from these conventional methods to more systematic, data-driven decision-making.

Machine learning (ML) and operations research (OR) offer promising approaches to address these challenges through novel optimization frameworks. By building surrogate models that capture the relationships between decision variables and targeted objectives, ML enables predictive modeling of complex manufacturing processes. OR then integrates these pre-trained models into a unified optimization framework, facilitating data-driven, rational, and scientifically grounded decisions that accelerate product development while minimizing experimental costs.

In this work, we present an ML- and OR-based framework that combines Bayesian optimization (BO) with first-principles knowledge. We demonstrate its effectiveness in solving industrial manufacturing optimization problems across additive manufacturing and thermoelectric material domains, including flash sintering, plasma sintering, and aerosol jet printing. Our framework accelerates the identification of optimal experimental conditions while reducing both economic and labor costs. Moreover, by incorporating physical knowledge in multiple ways, it is ideally suited for data-scarce, customized, and expensive experiments. It is a general framework that is not restricted to any single application domain.