Significance of Manufacturing Process Parameters in a Glassworks

Abstract

Abstract 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.