GOALI: Going Beyond Design of Experiments (DOE) - Process Improvement by Joint Adjustment and Monitoring

Pennsylvania State Univ University Park, University Park PA

Investigators

Abstract

The challenge for process control in many settings is that if a process is left alone it will tend to drift and wander away from target and as a result, quality will suffer. This is especially true in an industrial setting where both process inputs and outputs continuously change. Traditional process control has centered on detecting special causes that are suggested by significant patterns in the data which point to the existence of unexpected changes or signals. Many situations occur, however, where certain process signals are anticipated because they are characteristic of a system or operation. The approach to be used in this prjoect is to navigate to a good operating region using design of experiments (DOE) to evaluate a set of variables, then focus the control effort on the critical variables. Process transfer functions will be used to optimize the control adjustment of these variables and use Cuscore statistics to efficiently monitor the process by capitalizing on the structure of the anticipated signal. For process adjustment and monitoring, the following questions will be investigated: (1) What are the appropriate controllers and Cuscore statistics for common process characterizations? (2) How well will the new monitoring algorithms work in detecting signals? (3) How robust are the controllers and monitors to approximations of (or deviations from) their key parameters? The contributions of this research will include both empirical and theoretical developments to deliver improved output through better process understanding, monitoring, and control. In particular, the research is expected to provide a methodology for connecting the knowledge on experimental design and process improvement, provide a full set of adjustment and Cuscore monitoring algorithms and statistical evaluation of their properties, generate tools and charting algorithms to improve daily process operation and control, and demonstrate the feasibility of joint adjustment and monitoring for real industry applications. This work includes a partnership with Springs Window Fashions Division, Inc. (Middleton, WI). Springs is a leading manufacturer of home window treatments and their processing operations will serve as a test bed for implementation of the results. It also includes an opportunity for a graduate research assistant to work on the project and with the company.