Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression. (11th June 2019)

• Record Type: Journal Article
• Authors: Das, Prasun; Gauri, Susanta Kumar; Das, Anupam; Chatterjee, Debolina
• Is Part Of: International journal of productivity and quality management. Volume 27:Number 2(2019)
• Page Start: 177
• Page End: 195
• Publication Date: 2019-06-11
• Subjects: multi-stage manufacturing -- partial least square regression -- PLSR -- multi-block PLSR -- MBPLSR -- steel making -- variable contribution -- alloy chemistry -- rolling parameters -- strength -- ductility -- sensitivity; Quality control -- Management -- Periodicals; Industrial productivity -- Management -- Periodicals; 658.5005
• Languages: English
• ISSNs: 1746-6474

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 T 2 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.