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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.

Accurate prediction of exceedance probabilities is important in many applications. For example, in process planning and control, engineers should anticipate the risk that a product fails to meet its specification limits. Statistical comparison between candidate probability prediction methods is commonly performed using scoring rules, like the continuous ranked probability score (CRPS) and the logarithm score (LogS). In this work, a new scoring rule, the exceedance probability score, is proposed. The experiments in simulated and real industrial data show that the new scoring rule is useful in comparing and testing differences in the predictive accuracy of competitive probabilistic predictions in regression setting. The proposed scoring rule have some similarities with CRPS and LogS, but is more directly connected to the accuracy in the prediction of exceedance probabilities.

Accurate prediction of exceedance probabilities is important in many applications. For example, in process planning and control, engineers should anticipate the risk that a product fails to meet its specification limits. Statistical comparison between candidate probability prediction methods is commonly performed using scoring rules, like the continuous ranked probability score (CRPS) and the logarithm score (LogS). In this work, a new scoring rule, the exceedance probability score, is proposed. The experiments in simulated and real industrial data show that the new scoring rule is useful in comparing and testing differences in the predictive accuracy of competitive probabilistic predictions in regression setting. The proposed scoring rule have some similarities with CRPS and LogS, but is more directly connected to the accuracy in the prediction of exceedance probabilities.