Szczegóły publikacji
Opis bibliograficzny
Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks / Barbara MRZYGŁÓD, Marek Hawryluk, Zbigniew Gronostajski, Andrzej OPALIŃSKI, Marcin Kaszuba, Sławomir Polak, Paweł Widomski, Jacek Ziemba, Maciej Zwierzchowski // Archives of Civil and Mechanical Engineering / Polish Academy of Sciences. Wrocław Branch, Wrocław University of Technology ; ISSN 1644-9665. — 2018 — vol. 18 iss. 4, s. 1079-1091. — Bibliogr. s. 1090-1091, Abstr. — Publikacja dostępna online od: 2018-03-20
Autorzy (9)
- AGHMrzygłód Barbara
- Hawryluk Marek
- Gronostajski Zbigniew
- AGHOpaliński Andrzej
- Kaszuba Marcin
- Polak Sławomir
- Widomski Paweł
- Ziemba Jacek
- Zwierzchowski Maciej
Słowa kluczowe
Dane bibliometryczne
ID BaDAP | 113897 |
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Data dodania do BaDAP | 2018-05-28 |
Tekst źródłowy | URL |
DOI | 10.1016/j.acme.2018.02.010 |
Rok publikacji | 2018 |
Typ publikacji | artykuł w czasopiśmie |
Otwarty dostęp | |
Czasopismo/seria | Archives of Civil and Mechanical Engineering |
Abstract
This article concerns a decision-support system based on artificial neural networks (ANN) enabling analysis and forecasting of the durability of forging tools used in the hot forging process of a cover forging – a sealing element of the driveshaft in road freight vehicles. The process of knowledge acquisition, adopted neural network architecture and parameters of the developed network are presented. In addition, 3 variants of a hybrid layer (gas nitrided layer GN + PVD coating) were applied to the selected tools (punches applied in the 2nd top forging operation): GN/AlCrTiN, GN/AlCrTiSiN, and GN/CrN, in order to improve durability, and the resultant tools were also compared to standard tools (with only gas nitriding) and regenerated tools (after repair welding regeneration). The indispensable knowledge about the durability of selected forging tools (after various surface engineering variants), required for the process of learning, testing and validation for various neural network architectures was obtained from comprehensive, multi-year studies. These studies covered, among other things: operational observation of the forging process, macroscopic analysis combined with scanning of tools’ working surfaces, microhardness measurements, microstructural analysis and numerical modeling of the forging process. The developed machine-learning dataset was a collection of approx. 900 knowledge records. The input (independent) variables were: number of forgings manufactures, pressing forces, temperature on selected tool surfaces, friction path and type of protective layer applied to tool. Meanwhile, output (dependent) variables were: geometrical loss of tool material and percentage share of the four main destructive mechanisms. Obtained results indicate the validity of employing ANN-based IT tools to build decision-support systems for the purpose of analyzing and forecasting the durability of forging tools.