A nonlinear process monitoring strategy for a Metal Forming process. (2022)

• Record Type: Journal Article
• Authors: Kumar, Ashish; Das, Anupam
• Is Part Of: Materials today. Volume 59:Part 1(2022)
• Page Start: 368
• Page End: 372
• Publication Date: 2022
• Subjects: Metal Forming Process -- Rolling Mill Unit -- Kernel Principal Component Analysis -- Fault Detection; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.11.327
• Languages: English
• ISSNs: 2214-7853

Abstract

Highlights: The article highlights the development of Process Monitoring Strategy for a Metal Forming Process. Article developed addresses the issues of nonlinearity and collineairty amongst the measured characteristics. KPCA has been used for the development of the nominal model. Hotelling T 2 control chart has been used for the detection of fault(s) in the process. Abstract: The article deals with the development of a nonlinear statistical process monitoring strategy for a metal forming process. The developed strategy is based on Kernel Principal Component Analysis (KPCA) and has been adopted to address the issue of inherent nonlinearity amongst the measured characteristics. Fault Detection has been carried out by the employment of KPCA score based Hotelling T 2 chart. The devised strategy was applied over a Rolling Mill Unit (RMU) which comes under Bulk Metal Forming process. Observations pertaining to the Process and Feedstock Characteristics (PFC) of the RMU have been considered for the analysis. The devised monitoring strategy has been found to be efficient in carrying out the fault detection.