Article: Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression Journal: International Journal of Productivity and Quality Management (IJPQM) 2019 Vol.27 No.2 pp.177 - 195 Abstract: In order to have an understanding about the quality of steel product, involving multiple stages of manufacturing process, adequate assessment about the input-output relationship is necessary to ensure high-quality product. In this study, a high-strength low-alloy (HSLA) steel product quality, comprising of two stages of manufacturing, is modelled using partial least square regression (PLSR) and multi-block PLSR (MBPLSR) approaches. The alloy chemistry and rolling parameters are considered here as input variables along with strength and ductility of the finished steel as responses. Hotelling's T2 statistic is used for diagnosis of faults in batches of heat along with developing fault detection system through significant input variables. Both the modelling approaches are found to be useful for this purpose. However, the MBPLSR-based modelling approach is preferable since it helps to locate the source of the problem quicker at appropriate stages of operation with the help of input variables. Inderscience Publishers - linking academia, business and industry through research

Title: Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression

Authors: Prasun Das; Susanta Kumar Gauri; Anupam Das; Debolina Chatterjee

Addresses: SQC & OR Unit, Indian Statistical Institute, 203 B, T. Road, Kolkata-700108, India ' SQC & OR Unit, Indian Statistical Institute, 203 B, T. Road, Kolkata-700108, India ' Department of Mechanical Engineering, National Institute of Technology Patna, Patna, India ' SQC & OR Unit, Indian Statistical Institute, 203 B, T. Road, Kolkata-700108, India

Abstract: In order to have an understanding about the quality of steel product, involving multiple stages of manufacturing process, adequate assessment about the input-output relationship is necessary to ensure high-quality product. In this study, a high-strength low-alloy (HSLA) steel product quality, comprising of two stages of manufacturing, is modelled using partial least square regression (PLSR) and multi-block PLSR (MBPLSR) approaches. The alloy chemistry and rolling parameters are considered here as input variables along with strength and ductility of the finished steel as responses. Hotelling's T² statistic is used for diagnosis of faults in batches of heat along with developing fault detection system through significant input variables. Both the modelling approaches are found to be useful for this purpose. However, the MBPLSR-based modelling approach is preferable since it helps to locate the source of the problem quicker at appropriate stages of operation with the help of input variables.

Keywords: multi-stage manufacturing; partial least square regression; PLSR; multi-block PLSR; MBPLSR; steel making; variable contribution; alloy chemistry; rolling parameters; strength; ductility; sensitivity.

DOI: 10.1504/IJPQM.2019.100143

International Journal of Productivity and Quality Management, 2019 Vol.27 No.2, pp.177 - 195

Received: 06 Sep 2017
Accepted: 08 Jun 2018
Published online: 12 Jun 2019 *

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Title: Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression

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