XIV International Conference on Industrial Engineering and Environmental Protection – IIZS 2024, str. 171-175
АУТОР / AUTHOR(S): Miloš Madić , Milan Trifunović , Marko Kovačević
DOI: 10.46793/IIZS24.171M
САЖЕТАК / ABSTRACT:
Chip form is considered as important turning process performance measure. It is the prevalent criteria for the evaluation of industrial plastics machinability. Chips of unfavourable forms also create difficulties in production, such as stopping of the machine tool and damage to the machined surface. This factor is influenced by the cutting parameters in turning, which are the depth of cut, feed rate and cutting speed. This study focuses on the development of optimized support vector machine model for binary classification of chip forms in turning of commonly used industrial plastic, unreinforced polyoxymethylene copolymer POM-C.
КЉУЧНЕ РЕЧИ / KEYWORDS:
turning, chip, form, classification, POM-C, PCD, support vector machine
ЛИТЕРАТУРА / REFERENCES:
- Grzesik, W.: Advanced Machining Processes of Metallic Materials: Theory, Modelling, and Applications, Second Edition, Elsevier, Amsterdam,
- Madić, M., Trifunović, M., Janković, T.: Development and Analysis of a Surface Roughness Model in Dry Straight Turning of C45E Steel, Innovative Mechanical Engineering, Vol. 1, pp. 11-21,
- Sung, A.N., Loh, W.P., Ratnam, M.M.: Simulation Approach for Surface Roughness Interval Prediction in Finish Turning, International Journal of Simulation Modelling, 15, pp. 42-55, 2016.
- Rafai, N.H., Islam, M.N.: An Investigation into Dimensional Accuracy and Surface Finish Achievable in Dry Turning, Machining Science and Technology, 13, pp. 571- 589, 2009.
- Gundarneeya, T.P., Golakiya, V.D., Ambaliya, S.D., Patel, S.H.: Experimental Investigation of Process Parameters on Surface Roughness and Dimensional Accuracy in Hard Turning of EN24 Steel, Materials Today: Proceedings, Vol. 57, pp. 674-680, 2022.
- Hamasur, S.A., Abdalrahman, R.M.: The Effect of Tool’s Rake Angles and Infeed in Turning Polyamide 66, Engineering, Technology and Applied Science Research, Vol. 13, pp. 11204-11209,
- Korkmaz, M.E., Yaşar, N., Günay, M.: Numerical and Experimental Investigation of Cutting Forces in Turning of Nimonic 80A Superalloy, Engineering Science and Technology, an International Journal, Vol. 23, pp. 664-673,
- Shalaby, M.A., El Hakim, M.A., Abdelhameed, M.M., Krzanowski, J.E., Veldhuis, S.C., Dosbaeva, G.K.: Wear Mechanisms of Several Cutting Tool Materials in Hard Turning of High Carbon-Chromium Tool Steel, Tribology International, Vol. 70, pp. 148-154, 2014.
- Kuntoğlu, M., Sağlam, H.: Investigation of Progressive Tool Wear for Determining of Optimized Machining Parameters in Turning, Measurement, Vol. 140, pp. 427-436, 2019.
- Rao, J., Sreeamulu, D., Mathew, A.T.: Analysis of Tool Life During Turning Operation by Determining Optimal Process Parameters, Procedia Engineering, Vol. 97, pp. 241- 250, 2014.
- Mikołajczyk, , Nowicki, K., Bustillo, A., Pimenov, D.Y.: Predicting Tool Life in Turning Operations Using Neural Networks and Image Processing, Mechanical Systems and Signal Processing, Vol. 104, pp. 503-513, 2018.
- Rosa, S.D.N., Diniz, A.E., Andrade, C.L.F., Guesser, W.L.: Analysis of Tool Wear, Surface Roughness and Cutting Power in the Turning Process of Compact Graphite Irons with Different Titanium Content, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 32, pp. 234-240,
- Astakhov, V.P., Shvets, S.: The Assessment of Plastic Deformation in Metal Cutting, Journal of Materials Processing Technology, Vol. 146, pp. 193-202,
- Uysal, A., Jawahir, I.S.: Analysis of Slip-Line Model for Serrated Chip Formation in Orthogonal Machining of AISI 304 Stainless Steel Under Various Cooling/Lubricating Conditions, Journal of Manufacturing Processes, Vol. 67, pp. 447-460,
- Trifunović, M., Madić, M., Janković, P., Rodić, D., Gostimirović, M.: Investigation of cutting and specific cutting energy in turning of POMC using a PCD tool: Analysis and some optimization aspects, Journal of Cleaner Production, 303, Article ID: 127043, 19 pages, 2021.
- Barać, , Vitković, N., Stanković, Z., Rajić, M., Turudija, R.: Description and Utilization of an Educational Platform for Clean Production in Mechanical Engineering, Spectrum of Mechanical Engineering and Operational Research, Vol. 1, pp. 145-158, 2024.
- Lameski, , Zdravevski, E., Mingov, R., Kulakov, A.: SVM Parameter Tuning with Grid Search and Its Impact on Reduction of Model Over-fitting, Proceedings of the 15th
International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2015, pp. 464-474, Tianjin, China, 2015.
- Hsu, C.W., V, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification, Available at
https://www.datascienceassn.org/sites/default/files/Practical%20Guide%20to%20Sup port%20Vector%20Classification.pdf
- Penumuru, D.P., Muthuswamy, S., Karumbu, P.: Identification and classification of materials using machine vision and machine learning in the context of industry 4.0, Journal of Intelligent Manufacturing, Vol. 31, pp. 1229-1241,
- Fisher, , Rudin, C., Dominici, F.: All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously, Journal of Machine Learning Research, Vol. 20, pp. 1-81, 2019.
- Leppert, : Influence of cooling and lubrication on chip formation and its form in turning, Journal of Polish CIMAC, Vol. 6, pp. 97-106, 2011.