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)
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)
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 However, the question is when prediction accuracy has dropped to a level where the model can be considered out of date and in need of updating.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)
We examined the interpolation capabilities of learning methods using simulated data sets and a real data set. We compared five common learning methods for their generalisation capability on the boundaries of the training data set also; we examined the effects of the complexity of models on interpolation capability. Our main results were that there are(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)
Software applications used in the controlling and planning of production processes commonly make use of predictive statistical models. Changes in the process involve a more or less regular need for updating the prediction models on which the operational software applications are based. The objective of this article is • to provide information which helps to(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)