Engineering-Driven Statistical Adjustment and Calibration

• Activity Number: 372
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Technometrics
• Author(s): Roshan Joseph Vengazhiyil*+ and Huan Yan
• Companies: Georgia Institute of Technology and Georgia Institute of Technology
• Keywords: Computer experiments ; Gaussian process ; Nonlinear regression ; Quasi-Monte Carlo

Abstract

Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. This paper proposes a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micromachining process.