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

Łukasz Paśko

a:1:{s:5:"en_US";s:65:"Rzeszów University of Technology, Department of Computer Science";}

Aneta Kuś

Rzeszów University of Technology, Department of Computer Science


Abstract

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.


Keywords:

manufacturing process, glassworks, neural networks, bagging, manufacturing parameters

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Published
2021-09-22

Cited by

Paśko, Łukasz, & Kuś, A. (2021). Bootstrap Aggregation Technique for Evaluating the Significance of Manufacturing Process Parameters in the Glass Industry. Technical Sciences, 24(1), 135–155. https://doi.org/10.31648/ts.7062

Łukasz Paśko 
a:1:{s:5:"en_US";s:65:"Rzeszów University of Technology, Department of Computer Science";}
Aneta Kuś 
Rzeszów University of Technology, Department of Computer Science



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