Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression
by Prasun Das; Susanta Kumar Gauri; Anupam Das; Debolina Chatterjee
International Journal of Productivity and Quality Management (IJPQM), Vol. 27, No. 2, 2019

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.

Online publication date: Wed, 12-Jun-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Productivity and Quality Management (IJPQM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com