Collaborative Research: Applying Bayesian Predictive Modeling and Decision Theory to Milling Profit Optimization under Uncertainty

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The research objective of this collaborative research project is to establish a new paradigm for the selection of optimal milling parameters under uncertainty. The research plan includes two fundamental components: 1) develop a Bayesian predictive model; and 2) implement a decision making framework for maximizing profit. It will culminate in two significant outcomes. First, validation tests will be performed that compare production costs using cutting tool manufacturer-based recommendations for milling parameters to the new optimized result. Second, a software platform will be developed that guides users through the new approach to not only determine parameters for maximized profit, but also the optimal selection of experiments for new data collection. If successful, the new approach, which combines Bayesian predictive modeling and decision theory with machining modeling capabilities, will provide a fundamental departure from deterministic, model-based selection of milling parameters to a more realistic approach that incorporates the inherent uncertainty in model predictions. This will lead to new insights into optimal milling parameter selection by: 1) formulating milling as a decision problem under uncertainty; 2) providing the normative bases for calculation of value of experimentation in milling maximize expected utility; 3) providing algorithms for the real-time updating of milling performance uncertainties based on experimental results; 4) implementing the derived algorithms in software; and 5) conducting milling tests to verify the consistency of the normative Bayesian updating approach with experimental results. By enabling maximized profit under uncertainty for discrete part production by milling, this research will positively influence the nation?s economy and defense capabilities.