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Bootstrap Aggregation Technique for Evaluating the Significance of Manufacturing Process Parameters in the Glass Industry

Paśko Łukasz, Kuś Aneta

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2021

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nr 24(1)

135--155

EN

The article presents the application of the bootstrap aggregation technique to create a set of artificial neural networks (multilayer perceptron). The task of the set of neural networks is to predict the number of defective products on the basis of values of manufacturing process parameters, and to determine how the manufacturing process parameters affect the prediction result. For this purpose, four methods of determining the significance of the manufacturing process parameters have been proposed. These methods are based on the analysis of connection weights between neurons and the examination of prediction error generated by neural networks. The proposed methods take into account the fact that not a single neural network is used, but the set of networks. The article presents the research methodology as well as the results obtained for real data that come from a glassworks company and concern a production process of glass packaging. As a result of the research, it was found that it is justified to use a set of neural networks to predict the number of defective products in the glass industry, and besides, the significance of the manufacturing process parameters in the glassworks company was established using the developed set of neural networks.

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Application of machine learning and rough set theory in lean maintenance decision support system development

Antosz Katarzyna, Jasiulewicz-Kaczmarek Małgorzata, Paśko Łukasz, Zhang Chao, Wang Shaoping

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

695--708

EN

Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.

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Significance of Manufacturing Process Parameters in a Glassworks

Paśko Łukasz

Advances in Manufacturing Science and Technology

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2020

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Vol. 44, nr 2

39-45

EN

The article presents the use of artificial neural networks (multilayer perceptrons) to examine the significance of production process parameters. The considered problem relates to the occurrence of production periods with an increased number of defective products. The research aims to determine which of the 69 parameters of the manufacturing process most affect the number of defects. Two ways of expressing the parameters significance were used: using the sensitivity analysis and exploring the weights of connections between neurons. The results were determined using both single neural networks and a set of networks. The outcome from the research is the rankings of significance of the manufacturing process parameters. The analyzed data were obtained from a glassworks producing glass packaging.