Fundamentals of a recommendation system for the aluminum extrusion process based on data-driven modeling

• Autorzy: Perzyk Marcin , Kochański Andrzej , Kozłowski Jacek

Identyfikatory

DOI

10.7494/cmms.2022.4.0782

• Języki publikacji: EN

Abstrakty

EN

The aluminum profile extrusion process is briefly characterized in the paper, together with the presentation of historical, automatically recorded data. The initial selection of the important, widely understood, process parameters was made using statistical methods such as correlation analysis for continuous and categorical (discrete) variables and ‘inverse’ ANOVA and Kruskal–Wallis methods. These selected process variables were used as inputs for MLP-type neural models with two main product defects as the numerical outputs with values 0 and 1. A multi-variant development program was applied for the neural networks and the best neural models were utilized for finding the characteristic influence of the process parameters on the product quality. The final result of the research is the basis of a recommendation system for the significant process parameters that uses a combination of information from previous cases and neural models.

Słowa kluczowe

EN

aluminum extrusion; advisory system; product defect; data mining; neural networks

PL

system doradczy; wada produktu; eksploracja danych; sieci neuronowe

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Methods in Materials Science
• Rocznik: 2022
• Tom: Vol. 22, No. 4
• Strony: 173--188
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Perzyk Marcin

• Warsaw University of Technology, Institute of Materials Processing, Warsaw, Poland

• https://orcid.org/0000-0003-4901-7746

autor

Kochański Andrzej

• Warsaw University of Technology, Institute of Materials Processing, Warsaw, Poland

• https://orcid.org/0000-0002-1060-1390

autor

Kozłowski Jacek

• Warsaw University of Technology, Institute of Materials Processing, Warsaw, Poland

• https://orcid.org/0000-0002-5297-1509

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).