Methods of data mining for modelling of low-pressure heat treatment

• Autorzy: Wołowiec-Korecka E.

Identyfikatory

DOI

10.5604/01.3001.0010.7987

• Języki publikacji: EN

Abstrakty

EN

Purpose: This paper addresses the methods of the modelling of thermal and thermochemical processes used in computer-aided design, optimization and control of processes of thermal and thermochemical treatment in terms of obtaining real-time results of the calculations, which allows for observation of how an item changes during its treatment to respond immediately and to determine the parameters of a corrective process should any irregularities be detected. The main goal of the literature review was to develop a methodology for the design of functional and effective low-pressure processes of thermal and thermochemical treatments using effective calculation methods. Design/methodology/approach: A detailed analysis was conducted regarding the modelling methods with low-pressure carburizing and low-pressure nitriding. Findings: It was found the following criteria of methods selection of heat treatment modelling should be applied: data quality, data quantity, implementation speed, expected relationship complexity, economic and rational factors. Practical implications: Because of its non-equilibrium nature and transient states in the course of the processes computational support is particularly required in low-pressure thermochemical treatments. The primary goal of the simulation is to predict the course of the process and the final properties of the product, what ensures the repeatability of the process results. Originality/value: It was presented a synthetic presentation of modelling methods, in particular methods of artificial intelligence; it was also analysed the possibilities and risks associated with methods.

Słowa kluczowe

EN

heat treatment; low-pressure carburizing; low pressure nitriding; data mining

PL

obróbka cieplna; nawęglanie niskociśnieniowe; azotowanie niskociśnieniowe; eksploracja danych

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2017
• Tom: Vol. 85, nr 1
• Strony: 31--40
• Opis fizyczny: Bibliogr. 67 poz., rys., tab.

Twórcy

autor

Wołowiec-Korecka E.

• Institute of Material Science and Engineering, Lodz University of Technology, ul. Stefanowskiego 1/15, 90-924 Łódź, Poland

Bibliografia

• [1] L.A. Dobrzański, J. Trzaska, Application of neural networks for the prediction of continuous cooling transformation diagrams, Computational Materials Science 30/3-4 (2004) 251-259, doi: 10.1016/j.commatsci.2004.02.011.
• [2] P. Cavaliere, G. Zavarise, M. Perillo, Modeling of the carburizing and nitriding processes, Computational Materials Science 46 (2009) 26-35, doi: 10.1016/j.commatsci.2009.01.024.
• [3] I.B. Ozdemir, N. Lippmann, Modeling and simulation of surface reactions and reactive flow of a nitriding process, Surface and Coatings Technology 219 (2013) 151-162.
• [4] O. Karabelchtchikova, R.D. Sisson, Calculation of gas carburizing kinetics from carbon concentration profiles based on direct flux integration, Defect and Diffusion Forum 266 (2007) 171-180.
• [5] O. Karabelchtchikova, I.V. Rivero, S.M. Hsiang, Modeling of residual stress distribution in D2 steel via grinding dynamics using a second-order damping system, Journal of Materials Processing Technology 198/1-3 (2008) 313-322, doi: 10.1016/j.jmatprotec.2007.07.006.
• [6] P. Jacquet, D.R. Rousse, G. Bernard, M. Lambertin, A novel technique to motor carburizing process, Material Chemistry and Physics 77 (2002) 542-551.
• [7] H. Larsson, J. Agren, Gas nitriding of high vanadium steels – experiments and simulations, Metallurgical and Materials Transactions A 35/9 (2004) 2799-2802.
• [8] T. Malinova, S. Malinov, N. Pantev, Simulation of microhardness profiles for nitrocarburized surface layers by artificial neural network, Surface and Coatings Technology 135 (2001) 258-267.
• [9] S. Fang, M. Wang, Y. Wang, W. Qi, Evolutionary artificial neural network approach for predicting properties of Cu-15Ni-8Sn-0.4Si alloy, Transactions of Nonferrous Metals Society of China 18/5 (2008) 1223-1228.
• [10] Z. Gawroński, J. Sawicki, Technological surface layer selection for small module pitches of gear wheels working under cyclic contact loads, Materials Science Forum 513 (2006) 69-74, doi: 10.4028/www.scientific.net/MSF.513.69.
• [11] S. Lipa, J. Sawicki, E. Wołowiec, K. Dybowski, P. Kula, Method of determining the strain hardening of carburized elements In Ansys environment, Solid State Phenomena 240 (2016) 74-80.
• [12] Z. Gawroński, J. Sawicki, Toothed wheel optimization by means of the finite element analysis, Mechanics and Mechanical Engineering – International Journal 4/2 (2000) 183-189.
• [13] J. Dobrodziej, A. Mazurkiewicz, J. Wojutyński, J. Michalski, J. Ratajski, Model of controlling gas nitriding processes in the precise creation of surface layers with programmed properties, Journal of the Japan Society for Heat Treatment 49 (2009) 769-773.
• [14] J. Ratajski, R. Olik, T. Suszko, J. Dobrodziej, J. Michalski, Design, Control and In Situ Visualization of Gas Nitriding Processes, Sensors 10/1 (2010) 218-240, doi: 10.3390/s100100218.
• [15] S. Malinov, W. Sha, Software products for modeling and simulation in material science, Computational Materials Science 28 (2003) 179-198, doi: 10.1016/S0927-0256(03)00106-X.
• [16] M. Aliofkhazraei, A.S. Rouhaghdam, Neutral networks prediction of different frequencies effects on corrosion resistance obtained from pulsed nanocrystalline plasma electrolytic carburizing, Materials Letters 62 (2008) 2192-2195, doi: 10.1016/j.metlet.2007.11.052.
• [17] L.A. Dobrzański, W. Sitek, Comparision of hardenability calculation methods of the heat-treatable constructional steels, Journal of Materials Processing Technology 64 (1997) 117-126.
• [18] L.A. Dobrzański, W. Sitek, Application of a neutral network in modeling of hardenability of constructional steels, Journal of Materials Processing Technology 78 (1998) 59-66.
• [19] K. Genel, I. Ozbek, A. Kurt, C. Bindal, Boriding response of AISI W1 steel and use of artificial neural network for prediction of borided layer properties, Surface and Coatings Technology 160 (2002) 59-66.
• [20] K. Genel, Use of artificial neural network for prediction of ion nitrided case depth in Fe-Cr alloys, Material and Design 24/3 (2003) 203-207, doi: 10.1016/S0261-3069(03)00002-5.
• [21] S. Malinov, W. Sha, Application of artificial neural networks for modeling correlations in titanium alloys, Materials Science and Engineering A 365 (2004) 202-211, doi: 10.1016/j.msea.2003.09.029.
• [22] S. Malinov, W. Sha, Z. Guo, Application of artificial neural network for prediction of time-temperature-transformation diagrams in titanium alloys, Materials Science and Engineering A 283 (2000) 1-10.
• [23] N.S. Reddy, J. Krishnaiah, S. Hong, J. Lee, Modeling medium carbon steels by using artificial neural networks, Materials Science and Engineering A 508 (2009) 93-105, doi: 10.1016/j.msea.2008.12.022.
• [24] W. Sha, K.L. Edwards, The use of artificial neural networks in materials science based research, Material and Design 28 (2007) 1747-1752, doi: 10.1016/j.matdes.2007.02.009.
• [25] W. Sitek, L.A. Dobrzański, Application of genetic methods in materials’ design, Journal of Materials Processing Technology 164-165 (2005) 1607-1611, doi: 10.1016/j.jmatprotec.2005.01.005.
• [26] W. Sitek, L.A. Dobrzański, J. Zacłona, The modeling of high-speed steels’ properties using neutral networks, Journal of Materials Processing Technology 157-158 (2004) 245-249, doi: 10.1016/j.jmatprotec.2004.09.037.
• [27] R.G. Song, Q.Z. Zhang, Heat treatment technique optimization for 7175 aluminium alloy by an artificial network and a genetic algorithm, Journal of Materials Processing Technology 117 (2001) 84-88.
• [28] L. Xu, J. Xing, S. Wei, Y. Zhang, R. Long, Optimization of heat treatment technique of high-vanadium high-speed steel based on back-propagation neural networks, Material and Design 28 (2007) 1425-1432, doi: 10.1016/j.matdes.2006.03.022.
• [29] A. Zhecheva, S. Malinov, W. Sha, Simulation of microhardness profiles of titanium alloys after surface nitriding using artificial neural network, Surface and Coatings Technology 200 (2005) 2332-2342, doi: 10.1016/j.surfcoat.2004.10.018.
• [30] M. Korecki, P. Kula, J. Olejnik, New capabilities in HPGQ vacuum furnaces, Industrial Heating 3 (2011), http://www.industrialheating.com/articles/89834-new- capabilities-in-hpgq-vacuum-furnaces?v=preview (accessed June 2, 2017).
• [31] H. Klumper-Westkamp, H. Zoch, Sensor Application in Heat Treatment Processes for Enhancement of the Process Capability, Proceedings of the 3rd International Conference Heat Treating 2011, Qual. Heat Treat., IFHTSE, Wels, 2011, 98-105.
• [32] K. Evanson, G. Krauss, D. Medlin, M.J. Patel, Bencin Fatigue Behaviour of Vacuum Carburized AISI 8620 Steel, Proceedings of the 2nd International Conference Carburizing Nitriding Atmosphares, Cleveland, 1995, 61-69.
• [33] CFX-4.2 Solver, AEA Technology, CFDS Department, Harwell, 1997.
• [34] W.E. Dowling, T. Pattok, B.L. Ferguson, et al., Development of a carburizing and quenching simulation tool: Program Overview, HTM Journal of Heat Treatment and Materials 1 (1997) 1-6.
• [35] M. Dall’Oro, S. Villa, D. Valtolina, F.M. Montevecchi, The Carburizing of Steel with Hydrocarbon Gas in Low Relative Pressure, Proceedings of the 11th International Conference Heat Treatment and Surface Engineering, Florence, 1998, 487-492.
• [36] M.J. Berry, G. Linoff, Data mining techniques: for marketing, sales and customer support, John Willey & Sons, Hoboken, 1997.
• [37] M.J. Berry, G. Linoff, Mastering data mining, John Willey & Sons, Hoboken, 2000.
• [38] G. Migut, T. Demski, Technical information, 2012.
• [39] Statistica. Data Miner, StatSoft, Cracow, 2002.
• [40] S.M. Weiss, N. Indurakhya, Predictive data mining. A practical guide, Morgan Kaufmann Publisher, Burlington, 1998.
• [41] D. Braha, Data mining for design and manufacturing. Methods and applications, Kluwer Academic Publisher, Heidelberg, 2001.
• [42] P. McCullagh, J.A. Nelder, Generalized linear model, Chapman & Hall, London, 1999.
• [43] A. Agresti, An introduction to categorical data analysis, John Willey & Sons, Hoboken, 1996.
• [44] B. Ratner, Statistical modeling and analysis for database marketing, Chapman & Hall, London, 2003.
• [45] J. Han, M. Kamber, Data mining: Concepts and techniques, Academic Press, Waltham, 2001.
• [46] J. Friedman, Multivariate adaptive regression splines, The Annals of Statistics 19/1 (1991) 1-67.
• [47] D. Pyle, Data preparation for data mining, Academic Press, Waltham, 1999.
• [48] T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning, Springer-Verlag, Heidelberg, 2002.
• [49] P. Kula, J. Olejnik, P. Heilman, Hydrocarbon gas mixture for the under pressure carburizing of steel, EU 1 558 780, 2007.
• [50] P. Kula, J. Olejnik, P. Heilman, Hydrocarbon gas mixture for the under-pressure carburizing of steel, 7,513,958, 2009.
• [51] P. Kula, R. Pietrasik, K. Dybowski, Vacuum carburizing – process optimization, Journal of Materials Processing Technology 164-165 (2005) 876-881.
• [52] P. Kula, K. Dybowski, E. Wołowiec, R. Pietrasik, Boost-diffusion vacuum carburizing – process optimization, Vacuum 99 (2014) 175-179.
• [53] J. Dobrodziej, J. Wojutynski, K. Matecki, A. Gospodarczyk, J. Michalski, J. Tacikowski, P. Wach, J. Ratajski, R. Olik, The possibility of use of computer applications to design, simulation and verification of the processes of regulated gas nitriding, Surface Engineering 14/2 (2009) 34-45 (in Polish).
• [54] E. Wolowiec, The Application of Artificial Intelligence Methods in Development and Technical Realization of Surface Engineering Processes, PhD Thesis, Lodz University of Technology, 2009.
• [55] E. Wolowiec, P. Kula, B. Januszewicz, M. Korecki, Mathematical modelling the low-pressure nitriding process, Applied Mechanics and Materials 421 (2013) 377-383.
• [56] E. Wolowiec, Computer design of heat treatment processes, Lodz University of Technology, 2013.
• [57] M. Korecki, P. Kula, E. Wolowiec, M. Bazel, M. Sut, Low pressure carburizing and nitriding of fuel injection nozzles, Heat Treatment Reprots 3 (2014) 59-62.
• [58] L. Kolodziejczyk, Mathematical modeling of the process of vacuum carburizing, PhD Thesis, Lodz University of Technology, 2003.
• [59] E. Wolowiec, P. Kula, Ł Kolodziejczyk, K. Dybowski, M. Korecki, Mathematical model ling of the vacuum carburizing process, Thermal Processing for Gear Solutions 3-4 (2014) 34-40.
• [60] P. Kula, R. Pietrasik, E. Wolowiec, B. Januszewicz, A. Rzepkowski, Low-Pressure Nitriding according to the FineLPN Technology In Multi-purpose Vacuum Furnaces, Advanced Materials Research 586 (2012) 230-234.
• [61] P. Kula, E. Wolowiec, R. Pietrasik, K. Dybowski, B. Januszewicz, Non-steady state approach to the vacuum nitriding for tools, Vacuum 88 (2013) 1-7, doi: 10.1016/j.vacuum.2012.08.001.
• [62] R.G. Song, Q.Z. hang, Heat treatment optimization for 7175 aluminum alloy by genetic algorithm, Materials Science and Engineering C 17/1-2 (2001) 133-137, doi: 10.1016/S0928-4931(01)00321-6.
• [63] D. Goldberg, Genetic algorithms, Addison-Wesley Reading, Boston, 1989.
• [64] T. Velsker, M. Eerme, J. Majak, Articicial neural networks and evolutionary algorithms in engineering design, Journal of Achievements in Materials and Manufacturing Engineering 44/1 (2011) 88-95.
• [65] M. van Wie, Heuristics, science, and engineering, Mechanical Engineering – CIME 134 (2012) 8.
• [66] P. Kopecek, Selected heuristic methods used in industrial engineering, Procedia Engineering 69 (2014) 622-629, doi: 10.1016/j.proeng.2014.03.035.
• [67] E.S. Mistakidis, G.E. Stavroulakis, Nonconvex optimization in mechanics: algorithms, heuristics and engineering applications by the F.E.M., Kluwer Academic Publisher, Heidelberg, 1998.

Uwagi

PL

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