Statistical process control for multistage processes with non-repeating cyclic profiles

ABSTRACT

In many manufacturing processes, process data are observed in the form of time-based profiles, which may contain rich information for process monitoring and fault diagnosis. Most approaches currently available in profile monitoring focus on single-stage processes or multistage processes with repeating cyclic profiles. However, a number of manufacturing operations are performed in multiple stages, where non-repeating profiles are generated. For example, in a broaching process, non-repeating cyclic force profiles are generated by the interaction between each cutting tooth and the workpiece. This article presents a process monitoring method based on Partial Least Squares (PLS) regression models, where PLS regression models are used to characterize the correlation between consecutive stages. Instead of monitoring the non-repeating profiles directly, the residual profiles from the PLS models are monitored. A Group Exponentially Weighted Moving Average control chart is adopted to detect both global and local shifts. The performance of the proposed method is compared with conventional methods in a simulation study. Finally, a case study of a hexagonal broaching process is used to illustrate the effectiveness of the proposed methodology in process monitoring and fault diagnosis.

KEYWORDS:

• Broaching
• cyclic signals
• EWMA control chart
• multistage manufacturing processes
• partial least squares regression

Additional information

Notes on contributors

Wenmeng Tian

Wenmeng Tian is currently a Ph.D. student in the Grado Department of Industrial and Systems Engineering at Virginia Tech. She received her bachelor's degree in Industrial Engineering and master's degree in Management Science and Engineering, both from Tianjin University, China. Her research interests focus on high-density data modeling, monitoring, and prognostics in various manufacturing systems. She is a member of IIE.

Ran Jin

Ran Jin is an assistant professor at the Grado Department of Industrial and Systems Engineering at Virginia Tech. He received his Ph.D. in Industrial Engineering from Georgia Tech; his master's degrees in Industrial Engineering and in Statistics, both from the University of Michigan; and his bachelor's degree in Electronic Engineering from Tsinghua University. His research interests are in engineering-driven data fusion for manufacturing system modeling and performance improvements, such as the integration of data mining methods and engineering domain knowledge for multistage system modeling and variation reduction, and sensing, modeling, and optimization based on spatial correlated responses. He is a member of INFORMS, IIE, and ASME.

Tingting Huang

Tingting Huang is an assistant professor at the School of Reliability and Systems Engineering, Beihang University, Beijing, People's Republic of China. She was awarded her Ph.D. by the School of Reliability and Systems Engineering, Beihang University, in 2010. She worked as a postdoctoral researcher in the Department of Industrial Engineering, Tsinghua University, in 2011. She was awarded her master's degree by the Department of Industrial and Systems Engineering, Virginia Tech, in 2014. She was a visiting scholar in the Department of Industrial and Systems Engineering, Rutgers University in 2008. Her research interests involve accelerated life testing, accelerated degradation testing, and other reliability and environment testing technologies. Her recent work is on proportional hazards–odds model–based accelerated degradation testing.

Jaime A. Camelio

Jaime A. Camelio is currently the Rolls Royce Commonwealth Professor for Advanced Manufacturing in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He obtained his B.S. and M.S. in Mechanical Engineering from the Catholic University of Chile in 1994 and 1995, respectively. In 2002, he received his Ph.D. from the University of Michigan. His professional experience includes working as a consultant in the Automotive/Operations Practice at A.T. Kearney Inc. and as a Research Scientist in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor. His research interests are in assembly systems, intelligent manufacturing, process monitoring and control, and cyber-physical security in manufacturing. He has authored or co-authored more than 70 technical papers and holds one patent.