Data-Driven Optimization of Advanced Casting Processes

Abstract

The production of advanced cast components, such as those used in aerospace turbine applications, is limited by process variability that reduces yield and consistency. Many parameters can be monitored and adjusted along the casting chain, but not all of them are equally relevant to final part quality. This project, part of the Pratt & Whitney Center of Excellence for Solidification Science at UCI, identifies and prioritizes the parameters with the highest influence on part quality, develops data-driven models that quantify process sensitivities, and establishes a roadmap for optimizing them. Ensemble Bayesian networks trained on process data reveal which variables drive each type of defect, and Bayesian optimization then selects, within safe operating ranges, the settings that minimize the probability of a defect, so that a few informative production trials replace exhaustive testing. The methodology is being developed on wax injection, the first stage of the investment-casting process, and is designed to extend stage by stage to the entire casting workflow.