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

本合作研究计划的研究目标是建立一个新的范例，在不确定性下的最佳铣削参数的选择。研究计划包括两个基本组成部分：1）开发贝叶斯预测模型; 2）实施利润最大化的决策框架。它将最终导致两个重大成果。首先，将进行验证测试，将使用基于刀具制造商的铣削参数建议的生产成本与新的优化结果进行比较。第二，将开发一个软件平台，指导用户采用新方法，不仅确定最大利润的参数，而且确定新数据收集的最佳实验选择。如果成功的话，新的方法，结合贝叶斯预测建模和决策理论与加工建模能力，将提供一个从确定性的，基于模型的铣削参数选择的一个更现实的方法，将固有的不确定性模型预测的根本出发。这将导致新的见解，最佳铣削参数选择：1）制定铣削作为一个决策问题下的不确定性; 2）提供规范的基础，计算的实验价值在铣削最大化的预期效用; 3）提供算法，实时更新的铣削性能的不确定性的基础上的实验结果; 4）在软件中实现派生的算法;进行铣削试验，验证规范贝叶斯修正方法与试验结果的一致性。通过使不确定性下的离散零件生产铣削利润最大化，这项研究将产生积极影响的国家？的经济和防御能力。

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.