Significance of Manufacturing Process Parameters in a Glassworks

• Autorzy: Paśko Łukasz

Identyfikatory

DOI

10.2478/amst-2019-0009

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

neural networks; glass industry; glass packaging; significance of variables; sensitivity analysis; machine learning

PL

sieci neuronowe; przemysł szklarski; opakowania szklane; znaczenie zmiennych; analiza wrażliwości; nauczanie maszynowe

• Wydawca: Komitet Budowy Maszyn PAN ; De Gruyter
• Czasopismo: Advances in Manufacturing Science and Technology
• Rocznik: 2020
• Tom: Vol. 44, nr 2
• Strony: 39--45
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Paśko Łukasz

• Faculty of Mechanical Engineering and Aeronautics, Department of Computer Science, Rzeszów University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).