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The purpose of this study was to improve the dimensional accuracy of steel plate by updating the selection of combination parameters for slab design with statistical models. The generalised boosted regression model and the generalised additive models were used to predict the dimensional properties of the combination parameters with process factors. For real-life application, the modelling results were utilised to determine new combination parameter classes containing a larger number of process factors instead of only one. The research increased the knowledge of material sufficiency and the factors behind it. As a result, the new selection procedure is expected to increase yield and reduce the risk of rejection.

The purpose of this study was to improve the dimensional accuracy of steel plate by updating the selection of combination parameters for slab design with statistical models. The generalised boosted regression model and the generalised additive models were used to predict the dimensional properties of the combination parameters with process factors. For real-life application, the modelling results were utilised to determine new combination parameter classes containing a larger number of process factors instead of only one. The research increased the knowledge of material sufficiency and the factors behind it. As a result, the new selection procedure is expected to increase yield and reduce the risk of rejection.