Ensemble modeling for data fusion in manufacturing process scale-up

Abstract

In modern manufacturing process scale-up, design of experiments is widely used to identify optimal process settings, followed by production runs to validate these process settings. Both experimental data and observational data are collected in the manufacturing process. However, current methodologies often use a single type of data to model the process. This work presents an innovative method to efficiently model a manufacturing process by integrating the two types of data. An ensemble modeling strategy is proposed that utilizes the constrained likelihood approach, where the constraints incorporate the sequential nature and inherent features of the two types of data. It therefore achieves better estimation and prediction than conventional methods. Simulations and a case study in wafer manufacturing are provided to illustrate the merits of the proposed method.

Keywords:

• Data fusion
• ensemble model
• manufacturing scale-up
• model selection
• nonnegative garrote
• variation reduction

Additional information

Notes on contributors

Ran Jin

Ran Jin is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He received his Ph.D. degree in Industrial Engineering from Georgia Tech, master’s degrees in Industrial Engineering and in Statistics, both from the University of Michigan, Ann Arbor, and his bachelor’s degree in Electronic Engineering from Tsinghua University, China. His research interests are in engineering–driven data fusion for manufacturing system modeling and performance improvement, such as quality modeling and control in manufacturing scale-up, and sensing, modeling, and optimization based on spatial correlated responses. He is a member of INFORMS, IIE, ASME, and ASEE.

Xinwei Deng

Xinwei Deng is an Assistant Professor in the Department of Statistics at Virginia Tech. He received his Ph.D. degree in Industrial Engineering from Georgia Tech and his bachelor’s degree in Mathematics from Nanjing University, China. His research interests are in statistical modeling and analysis of massive data, including high-dimensional classification, graphical model estimation, interface between experimental design and machine learning, and statistical approaches to nanotechnology. He is a member of INFORMS and ASA.