Development of an Approximation Model of Selected Properties of Model Materials Used for Simulations of Bulk Metal Plastic Forming Processes Using Induction of Decision Trees

• Autorzy: Hawryluk M. , Wilk-Kołodziejczyk D. , Regulski K. , Głowacki M.

Identyfikatory

DOI

10.24425/amm.2019.129497

• Języki publikacji: EN

Abstrakty

EN

The article discusses the development of an approximation model of selected plastic and mechanical properties obtained from compression tests of model materials used in physical modeling. The use of physical modeling with the use of soft model materials such as a synthetic wax branch with various modifiers is a popular tool used as an alternative or verification of numerical modeling of bulk metal forming processes. In order to develop an algorithm to facilitate the choice of material model to simulate the behavior of real-metallic materials used in industrial production processes the induction of decision trees was used. First of all, the Statistica program was used for data mining, which made it possible to determine / find the relationship between the percentage of particular constituents of the model material (base material and modifiers) and yield strength, critical and maximum strain, and provide the opportunity to indicate the most important variables determining the shape of the stress - strain curve. Next, using the induction of decision trees, an approximation model was developed, which allowed to create an algorithm facilitating the selection of individual modifying components. The last stage of the research was verification of the correctness of the developed algorithm. The obtained research results indicate the possibility of using decision tree induction to approximate selected properties of modeling materials simulating the behavior of real materials, thus eliminating the need for costly and time-consuming experiments carried out on metallic material.

Słowa kluczowe

EN

regression tree induction; properties approximation; physical modelling; soft model materials

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2019
• Tom: Vol. 64, iss. 3
• Strony: 1073--1085
• Opis fizyczny: Bibliogr. 36 poz., fot., rys., tab., wzory

Twórcy

autor

Hawryluk M.

• Wroclaw University of Science and Technology, Lukasiewicza 5, 50-371, Wrocław, Poland

autor

Wilk-Kołodziejczyk D.

• AGH University of Science and Technology, Department of Applied Computer Science and Modelling, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Regulski K.

• AGH University of Science and Technology, Department of Applied Computer Science and Modelling, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Głowacki M.

• AGH University of Science and Technology, Department of Applied Computer Science and Modelling, Al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

• [1] A.E.M. Pertence, P. R. Cetlin. Similarity of ductility between model and real materials, Journal of Material Processing technology 103, 434-438 (2000).
• [2] B.P.P.A. Gouveia, J.M.C. Rodrigues, P.A.F. Martins, T. Bay Physical and numerical simulation of the round-to-square forward extrusion, Journal of Mechanical Working Technology 112, 244-251 (2001).
• [3] D. Wilk-Kołodziejczyk, K. Regulski, G. Gumienny, B. Kacprzyk, S. Kluska-Nawarecka, K. Jaśkowiec, Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements, International Journal of Advanced Manufacturing Technology 95, 3127-3139 (2018).
• [4] G. C. Gingher, G. Padjen. Hot strip mill edging practices and plasticine modelling, 34th MWSP Conf. Proc., ISS-AIME 30, 3-12 (1993).
• [5] H. W. Shin, et. al. A Study on the rolling of I-section Beams, International Journal of Machine Tools and Manufacture 34 (2), 147-160 (1994).
• [6] I. Olejarczyk-Wożeńska, A. Adrian, H. Adrian, B. Mrzygłód. Parametric representation of TTT diagrams of ADI cast iron. Archives of Metallurgy and Materials 57, 613-617 (2012). DOI: 10.2478/v10172-012-0065-9.
• [7] J. Alder, K. A. Phillips. The effect of strain-rate and temperature on the resistance of aluminum, copper and steel to compression, Journalof the Institute Metals 83, 80-88 (1954).
• [8] J. Boucly, J. Oudin, Y. Ravalard. Simulation of ring rolling with new wax-based model material on a flexible experimental machine, Journal of Mechanical Working Technology 16 (2), 119-143 (1988).
• [9] J. H. Hollomon. Tensile deformation. Trans. AIME 162, 268-290 (1945).
• [10] J. R. Quinlan. Induction on decision trees. Machine Learning, Kluwer Academic Publishers, Boston (1986).
• [11] K. Regulski, D. Szeliga, J. Kusiak. Data exploration approach versus sensitivity analysis for optimization of metal forming processes. Key Engineering Materials 611-612, 1390-1395 (2014).
• [12] K. Swiatkowski, R. Cacko. Investigations of new wax-based model materials simulating metal working process 72 (2), 267-271 (1997).
• [13] K. Swiatkowski. Physical modelling of metal working processes using wax-based model materials 72 (2), 272-276 (1997).
• [14] L. Breinman, et al. Classification and regression trees. Chapman and Hall, London (1993).
• [15] Ł. Wojcik, K. Lis, Z. Pater. Plastometric tests for plasticine as physical modelling material, Open Engineering 6 (1), 653-659 (2016).
• [16] M. Arentoft. Prevention of defects in forging by numerical and physical simulation, Technical University of Denmark Institute of Manufacturing Engineering 1996.
• [17] M. Arentoft, P. Henningsen, N. Bay, T. Wanheim. Simulations of defects in metal forming, Journal of Mechanical Working Techno-logy 45, 527-532 (1994).
• [18] M. Hawryluk, B. Mrzygłód. A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks (ANN). Eksploatacja i Niezawodność - Maintenance and Reliability 19 (3), 338-348 (2017), http://www.ein.org.pl/sites/default/files/2017-03-04p.pdf.
• [19] M. Hawryluk, J. Jakubik. Analysis of forging defects for selected industrial die forging processes. Engineering Failure Analysis 59, 396-409 (2016).
• [20] M. Hawryluk, S. Polak, Z. Gronostajski, K. Jaśkiewicz, Application of physical similarity utilizing soft modeling materials and numerical simulations to analyse the plastic flow of UC1 steel and the evolution of forces in a specific multi-operational industrial precision forging process with a constant-velocity joint housing. Experimental Techniques, https://doi.org/10.1007/s40799-018-0288-4.
• [21] M. Hawryluk. The inf luence of the condition of plastic similarity on the accuracy of physical modeling of extrusion processes, PhD thesis, Wroclaw University of Science and Technology, Wrocław 2006.
• [22] M. Itoh. Determination of forming limits of thick sheet in compression bending by using model material, The Technical University of Denmark Institute of Manufacturing Engineering, Journal of Mechanical Working Technology 12, 269-277 (1985).
• [23] M. Young-Hoon, et al. Physical modelling of edge rolling in plate mill with plasticine. Steel Research 64 (11), 557-563 (1993).
• [24] M. J. Adams, et al. A Two roll mill as a rheometer for pastes, material resume social symposium process, Material Research Society 289, 237-257 (1989).
• [25] O. C. Zienkiewicz. Finite Element Method, McGraw Hill (1977).
• [26] P. G. Maropoulos. Review of research in tooling technology, process modelling and process planing. Part II: Process planing. Computer Integrated Manufacturing Systems 8 (1), 13-20 (1995).
• [27] D. Wilk-Kołodziejczyk, K. Regulski, G. Gumienny, Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine, The International Journal of Advanced Manufacturing Technology 87, 1077-1093 (2016).
• [28] T. Gangopadhyay, D. Kumar D. I. Pratihar. Expert system to predict forging load and axial stress. Applied Soft Computing 11(1), 744-753 (2014).
• [29] T. Hill, P. Lewicki. STATISTICS methods and applications. Stat-Soft Tulsa (2007).
• [30] T. Wanheim. Physical modelling of metal processing. Proces technics Institut, Laboratoriet for Mekaniske Materialeprocesser; Danmarks Teknisk Hojkole, 1988; Danmark.
• [31] V. Vazquez, T. Altan. New concepts in die design - physical and computer modelling applications, Journal of Material Processing Technology 98, 212-223 (2000).
• [32] Z. Górny, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski. Methodology for the construction of a rule-based knowledge base enabling the selection of appropriate bronze heat treatment parameters using rough sets. Archives of Metallurgy and Materials 60, 309-315 (2015). DOI: 10.1515/amm-2015-0050.
• [33] Z. Gronostajski, et al. The expert system supporting the assessment of the durability of forging tools. The International Journal of Advanced Manufacturing Technology 82 (9), 1973-1991 (2015).
• [34] Z. Gronostajski, et. al. Analysis of forging process of constant velocity joint body. Steel Research International 1:547-554 (2008).
• [35] Z. Gronostajski, M. Hawryluk. Analysis of metal forming processes by using physical modelling and new plastic similarity condition. 10th ESAFORM Conference on Material Forming AIP Conference Proceedings, ISSN 0094-243X 907, Zaragoza, Spain 608-613 (2007).
• [36] Z. Gronostajski, M. Hawryluk. Materials used in physical modeling, Mechanical Review 7-8, 42-46 (2005).

Uwagi

EN

1. This study was founded by National Centre for Research and Development, Poland (grant no. TECHMATSTRATEG1/348491/10/NCBR/2017).

PL

2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).