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

Aneta Kuś,Łukasz Paśko

doi:10.31648/ts.7062

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.

Highlights

Nowadays, it is difficult to find an industry that does not use artificial neural networks (ANNs)
The RMSE value calculated from the prediction results of the entire set of ANNs is 18.31 for the test subset
Prediction using the naive method has the RMSE error of 27.84. This value is much higher than the RMSE determined for the set of ANNs, which proves that prediction using developed ANNs is more effective

Summary

Introduction

It is difficult to find an industry that does not use artificial neural networks (ANNs). Neural modelling in the agriculture field was used by Niedbała et al (2015) to predict starch content in potatoes. Staying on the topic of the non-obvious application of ANN in agriculture, and in mechanical engineering, Francik (2006) describes the use of the method of forecasting time series with the use of ANN to characterize agricultural machines. The use of ANNs in mechanical engineering has been discussed for many years; already Lefik (2005) described the applications of ANNs in mechanics and engineering, but it can certainly be said that this application is still showing an upward trend and we are witnessing incredible developments in the field of ANNs

Objectives

Methods

Results

Conclusion