An analyze and prediction system the loss of material of forging tools using artificial neural networks

  • Marek Robert Hawryluk Wroclaw University of Sicence and Technology
  • Barbara Mrzygłód AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Krakow, Poland

Abstract


The article presents the use of artificial neural networks (ANN), to build a system of analysis and forecasting the durability of forging tools and the process of acquiring the source knowledge necessary for the network learning process. In particular, the prediction of the geometrical loss of tool material after different surface treatment variants.The methodology of developing neural network models and their quality parameters is also presented. The standard single-layer MLP networks were used here, whose quality parameters are at a high level and the results presented with their participation give satisfactory results in line with technological practice.The data used in the learning process come from extensive comprehensive performance tests of forging tools operating under extreme operating conditions (cyclic mechanical and tehermal loads). The parameterization of factors important for the selected forging process was made and a database was developed, including 900 knowledge vectors, each of which provided information on the size of the geometrical loss of the tool material (explained variables). The value of wear was determined for set values ​​of explanatory variables such as: number of forgings, pressure, temperature on selected tool surfaces, friction path and the variant of the applied surface treatment. The results presented at work confirmed by expert technologists have clearly an application character, because based on the presented solutions, you can choose the optimal treatment and apply appropriate preventive measures that will extend the service life.

Author Biographies

Marek Robert Hawryluk, Wroclaw University of Sicence and Technology

Marek Hawryluk, phd. Hab, eng., born in 1977, achieved his Msc in the Mechnical Faculty at the Wroclaw University of Science and Technology (WUST),  Poland. He is currently assistant professor at WUST in the department of metal forming and metrology. He is an author or a coauthor of approx. 80 papers (over 30 with if in web of science) and 10 solutions used in the industry. His research interests cover numerical and physical modeling of metal forming processes and materials science, the durability of forging tools, optimization of metal forming technology, new approaches and modifications of forging process, application of new measurement and control systems in industry. He is also the lecture at WUST, graduate theses, organization (cooperation within eu and domestic projects, elaboration of project proposals, project management, organization of scientific conferences and trips trips to industrial enterprises.

Barbara Mrzygłód, AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Krakow, Poland
PhD Barbara Mrzygłod, born in 1979, achieved her MSc in the Mechnical Faculty at the AGH – University of Science and Technologyin Krakow),  Poland, in 2003 and received her DSc from the Faculty of Metals Engineering and Industrial Computer Science, also at  AGH She is currently Assistant Professor at AGH in Faculty of Metals Engineering and Industrial Computer Science. She is an author or a coauthor of approx. 60 papers. Her research interests cover numerical and IT systems mainly of metal forming and casting processes as well as materials science, the durability of forging tools, optimization of metal forming technology, new approaches. She is also the lecture at AGH, graduate theses etc.

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Published
2018/12/27
How to Cite
Hawryluk, M. R., & Mrzygłód, B. (2018). An analyze and prediction system the loss of material of forging tools using artificial neural networks. Journal of Mining and Metallurgy, Section B: Metallurgy, 54(3), 323. Retrieved from https://aseestant.ceon.rs/index.php/jmm/article/view/17192
Issue
Vol. 54 No. 3 (2018)
Section
Original Scientific Paper

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