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.
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