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Wear of the working surfaces of the forging dies in the process of manufacturing products with the die forging technique leads to deterioration of their operational properties as well as their technological quality. A characteristic feature of production in small and medium-sized enterprises is the high variability of the product range and short production series, which can be repeated in the case of re-orders by customers. In this type of production conditions, a technological criterion in form of – a change in the characteristic and selected dimension of forging is usually used to assess the quality of products. An important problem is, whether by taking up another order for a series of the same type of product, it will be possible to implement it with the existing die, or should a new die be made? As a result of the research carried out in the company implementing this type of contract, a procedure was proposed for forecasting the abrasive wear of die working surfaces on the basis of a technological criterion, easy to determine in the conditions of small and medium-sized enterprises. The paper presents the results of the wear assessment of a die made out of hot-work tool steel X37CrMoV5-1 (WCL) and dies made of 42CrMo4 alloy structural steel with hardfacing working surfaces by F-818 wire. To determine and forecast the process of die wear, a mathematical model in the form of neural networks was used. Their task was to forecast the ratio of the increment in introduced wear intensity indicator to the number of forgings made during the process. Taking into account
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
27--32
Opis fizyczny
Bibliogr. 18 poz., rys., tab. wykr.
Twórcy
autor
- Cracow University of Technology, Faculty of Mechanical Engineering, Poland, phone: +4877 374 3255
autor
- Jasło, Poland
autor
- Cracow University of Technology, Faculty of Mechanical Engineering, Poland
Bibliografia
- Azari, A., Poursina, M. and Poursina, D. (2014). Radial forging force prediction through MR, ANN and ANFIS models. Neural Computing & Applications, 25, 3–4, 849–858.
- Ciancio C., Citrea T., Ambrogio G., Filice L., Musmanno R. (2015). Design of a high performance predictive tool for forging operation. Procedia CIPR 33, 173–178.
- Gronostajski, Z., Hawryluk, M., Zwierzchowski, M., Kaszuba, M. and Niechajowicz, A. (2011). Description of the wear phenomena of hot forging dies for a gearbox disc. [In Polish]. Metallurgist – Metallurgical News, 78, 8, 607–611.
- Gronostajski, Z., Hawryluk, M., Kaszuba, M., Marciniak, M., Niechajowicz, A. and Polak S. (2015). The expert system supporting the assessment of the durability of forging tools. The International Journal of Advanced Manufacturing Technology, 82, 9, 1973–1991.
- Gronostajski, Z., Kaszuba, M., Hawryluk, M. and Zwierzchowski, M. (2014). A review of the degradation mechanisms of the hot forging tools. Archives of Civil and Mechanical Engineering, 14, 528–539.
- Gronostajski, Z., Kaszuba, M., Polak, S., Zwierzchowski, M., Niechajowicz, A. and Hawryluk, M. (2016). The failure mechanisms of hot forging dies. Materials Science and Engineering. A, Structural Materials: Properties, Microstructure and Processing, 657, 147–160.
- Gubán, M., Kása, R., Takács, D. and Mihai, Avornicului M. (2019). Trends of Using Artificial Intelligence in Measuring Innovation Potential. Management and Production Engineering Review, 10, 2, 3–15. doi: 10.24425/mper.2019.129564.
- Hawryluk, M. (2016a). Methods of analyzing and increasing the durability of forging tools used in hot die forging processes [in Polish], Monographic series of problems of exploitation and construction of machines, ISBN 97883-7789-410-1. Ed. Scientific ITE – PIB, Radom.
- Hawryluk, M. (2016b). Review of selected methods of increasing the life of forging tools in hot die forging processes. Archives of Civil and Mechanical Engineering, 16, 845–866, doi: 10.1016/j.acme.2016.06.001.
- Hawryluk, M. and Mrzygłód, B. (2018). A system of analysis and prediction of the loss of forging tool material applying artificial neural networks. Journal of Mining and Metallurgy. Section B: Metallurgy, 54, 3, 323–337. doi: 10.2298/JMMB180417023H.
- Hawryluk, M. and Mrzygłód, B. (2017). A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks (ANN). Maintenance and Reliability, 19, 3, 338–348.
- Kocańda, A. and Czyżewski, P. (2000). Computer analysis of abrasive wear of forging dies [In Polish], 14th Scientific and Technical Conference of Plastic Working “Design and Technology of Moldings”, Poznan.
- Krajewska-Śpiewak, J. (2016). Application of neural networks in condition’s modeling of the surface layer. Business management, 1.
- Mrzygłód, B., Hawryluk, M. and Gronostajski, Z. (2018). Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks. Archives of Civil and Mechanical Engineering, 18, 4, 1079–1091.
- Rauch, L., Chmura, A., Gronostajski, Z., Pietrzyk, M. and Zwierzchowski M. (2016). Cellular automata model for prediction of crack initiation and propagation in hot forging tools. Archives of Civil and Mechanical Engineering, 16, 3, 437–447.
- Turek, J., Okoński, S. and Piekoszewski W. (2012). Testing the abrasion resistance of overlay layers and tool steels for forging dies. [In Polish]. Metal Forming, Poznan.
- Turek, J. (2019). Structure and properties of surfacing layers on blanks of forging dies [in Polish], Krakow University of Technology Publishing House, Kraków, pp. 224, ISBN 978-83-65991-82-9
- Young, M. (1989). The Technical Writer’s Handbook, Mill Valley, CA. University Science.
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)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c9737477-dade-4ee1-ae07-a8c0adeb9c6b