Satu Tamminen

Exceedance Probability Score: A Novel Measure for Comparing Probabilistic Predictions

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.

Quantile regression model for impact toughness estimation

The purpose of this study was to develop a product design model for estimating the impact toughness of low-alloy steel plates. The rejection probability in a Charpy-V test (CVT) is predicted with process variables and chemical composition. The proposed method is suitable for the whole production line of a steel plate mill, including all grades of steel in production. The quantile regression model was compared to the joint model of mean and dispersion and the constant variance model. The quantile regression model proved out to be the most effective method for modelling a highly complicated property at this extent. Next, the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost.

Modelling of Charpy V test rejection probability

Abstract The purpose of this study was to develop a product design model for estimating the impact toughness of low alloy steel plates. The rejection probability in a Charpy V test is predicted with process variables and chemical composition. Joint modelling of the mean and deviation was used in order to improve the results. The proposed method is suitable for the whole production line, including all grades of steel in production and it is not restricted to a few test temperatures. Using the proposed model the product design group could have recognised most of the rejections before production. Next, the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost.

Visualizing Predicted and Observed Densities Jointly with Beanplot

Beanplot is a graphical method for visualizing univariate distributions. Density forecasts have an important role to play in many applications. Although graphical methods are widely used for illustrating distributions, suitable graphical methods to help for the purposes of analysis and comparison of density forecasters do not exist. This article explains how density forecasts and related observed densities are visualized parallel using beanplots in different groups of data. The visualization method is illustrated with industrial and simulated data. The functionality extends the plotting function of R package beanplot and the developed functions are made available for R programming language.

Exceedance probability estimation for a quality test consisting of multiple measurements

The purpose of this study was to develop methods for exceedance probability estimation in the case of highly scattered measurement sets. The situation may occur when product quality is verified with several test samples, and thus, traditional point prediction based modelling methods are not sufficient. Density forecasting methods are needed when not only the mean but also the deviance and the distribution shape of the response depend on the explanatory variables. Furthermore, with probability predictors, the ranking methods for the model selection should be chosen carefully, when models trained with different methods are compared. In this article, the impact toughness of the steel products was modelled. The rejection probability in Charpy-V quality test was predicted with mean and deviation models, distribution shape model and quantile regression model. The proposed methods were employed in two steel manufacturing applications with different distributional properties.

Product design model for impact toughness estimation in steel plate manufacturing

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 valuable information.The method is suitable for all steel grades in production and it is not restricted to a few test temperatures. There were differences between the performances of different product groups, but overall performance was promising. Next the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost.

Self-organizing maps in adaptive health monitoring

A method for health monitoring is considered. Measured physical signals have been dynamically classified to low-, middle- or high-levels and a self-organizing map (SOM) has been utilized to combine the information. The data were collected during spring 1996 and consist of over eight weeks of physical measurements and diaries recorded in a home environment by four test subjects. The research shows that this method can be used to monitor the system of a human being. The system finds some daily structures as well as differences between weekdays and weekend. The physical activities have much stronger effect on the signals than mental stress states, which show no clear clustering on maps.

From Measurements to Knowledge - Online Quality Monitoring and Smart Manufacturing

The purpose of this study was to develop an innovative supervisor system to assist the operators in an industrial manufacturing process to help discover new alternative solutions for improving both the products and the manufacturing process.

Improving the yield of steel plates by updating the slab design with statistical models

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.

Steel Property and Process Models for Quality Control and Optimization

High quality and low variability in the properties of the products are the main goals in manufacturing. The quality of the product is verified by testing different properties. It can be improved with models developed for event prediction. This paper presents with application examples the modelling steps required for effective process modelling. First, the pre-processing and feature extraction phase are illustrated. The modelling phase concentrates especially on the heteroscedasticity problem that is commonly present in industrial applications. The process monitoring and control parameter optimization based on these models is presented, as well as the solution for the lack of observations for the dependent variable. Many of the developed models are in daily use in different process states in steel industry. They enable the design of new products and the analysis of the effects of different process parameters on variability reduction. The proposed methods are application independent.