Statistical process control for multistage processes with non-repeating cyclic profiles. (4th March 2017)

• Record Type: Journal Article
• Authors: Tian, Wenmeng; Jin, Ran; Huang, Tingting; Camelio, Jaime A.
• Is Part Of: IISE transactions. Volume 49:Number 3(2017)
• Page Start: 320
• Page End: 331
• Publication Date: 2017-03-04
• Subjects: Broaching -- cyclic signals -- EWMA control chart -- multistage manufacturing processes -- partial least squares regression; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/0740817X.2016.1241454
• Languages: English
• ISSNs: 2472-5854

Abstract

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.