An Engineering-Statistical Approach to Predictive Modeling and Robust Optimization with Applications to Machining

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

The objective of this research is to develop a general framework for predictive modeling and robust optimization with applications to machining processes. The approach is to use Bayesian methods to integrate physics-based engineering models with experiment-based statistical models. The resulting models are called engineering-statistical models. The engineering models in machining are mostly deterministic in nature and often suffer from uncertainties due to model assumptions and unknown parameters, whereas the statistical models are expensive to develop and have poor predictive capability outside the experimental range. This project's engineering-statistical modeling approach overcomes these limitations and thus is expected to perform better. The approach will be developed and validated using two machining processes: laser assisted mechanical micromachining and conventional turning. If successful, the research will lead to a new modeling approach that can be used for making better prediction, control, and optimization of machining processes. The research is expected to make a great impact in industrial applications, because engineers in industries often do not have the time to identify the mistakes in the underlying assumptions of engineering models, develop new theory, and make corrections to the prediction. The new approach can be quickly applied to correct the engineering model based on the data from their processes and can be used for prediction and optimization. Moreover, the approach can be used for model refinement and can pin-point where the mistake might have happened and help the researchers to develop a better theory. The approach is general and is applicable to other manufacturing processes such as forming and joining. Because of the efficient use of information through simulations and experiments, it is envisaged that the approach will help in cutting down manufacturing costs by improving yield and reducing waste. The interdisciplinary nature of the project will result in rigorous training of a diverse group of students in manufacturing science and statistical methods, and thereby meet a critical need of the manufacturing industry.