Ilmari Juutilainen

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Statistical models that predict the tensile strength of low-alloyed steel plates using the element concentrations and some variables of the rolling process were developed. The purpose of the work was to develop a new predicting model for Rautaruukki's steel plate mill. The model will be used mainly in the product design of steel plates. The standard(More)
When new data are obtained or simply when time goes by, the prediction accuracy of models in use may decrease. However, the question is when prediction accuracy has dropped to a level where the model can be considered out of date and in need of updating. This article describes a method that was developed for detecting the need for a model update. The method(More)
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a(More)
Different industries utilize statistical prediction models that predict the product properties in process planning, control, and optimization. An important aim is to decrease the number of disqualifications. The model can prevent disqualifications efficiently if the disqualification probability is predicted accurately. This study gives step-by-step(More)
The purpose of this study was to develop a product design model for impact toughness estimation of low-alloy steel plates. Based on these estimates, the rejection probability of steel plates can be approximated. The target variable was formulated from three Charpy-V measurements with a LIB transformation, because the mean of the measurements would have lost(More)