Fuzzy Modeling and Parametric Analysis of Non-Traditional Machining Processes

• Autorzy: Shankar Chakraborty , Partha Protim Das
• Języki publikacji: EN

Abstrakty

EN

The application of artificial intelligence (AI) in modeling of various machining processes has been the topic of immense interest among the researchers since several years. In this direction, the principle of fuzzy logic, a paradigm of AI technique, is effectively being utilized to predict various performance measures (responses) and control the parametric settings of those machining processes. This paper presents the application of fuzzy logic to model two non-traditional machining (NTM) processes, i.e. electrical discharge machining (EDM) and electrochemical machining (ECM) processes, while identifying the relationships present between the process parameters and the measured responses. Moreover, the interaction plots which are developed based on the past experimental observations depict the effects of changing values of different process parameters on the measured responses. The predicted response values derived from the developed models are observed to be in close agreement with those as investigated during the past experimental runs. The interaction plots also play significant roles in identifying the optimal parametric combinations so as to achieve the desired responses for the considered NTM processes. (original abstract)

Słowa kluczowe

EN

Industry 4.0; Production process; Fuzzy logic

PL

Przemysł 4.0; Proces produkcji; Logika rozmyta

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2019
• Tom: 10
• Numer: nr 3
• Strony: 111--123

Twórcy

autor

Shankar Chakraborty

• Jadavpur University, Kolkata, India

autor

Partha Protim Das

• Sikkim Manipal University, India

Bibliografia

• Chakraborty S., Dey, S., QFD-based expert system for non-traditional machining processes selection, Expert Systems with Applications, 32, 4, 1208- 1217, 2007.
• Bhattacharyya B., Munda J., Malapati M., Advancement in electrochemical micro-machining, International Journal of Machine Tools and Manufacture, 44, 15, 1577-1589, 2004.
• Ho K.H., Newman S.T., State of the art electrical discharge machining (EDM), International Journal of Machine Tools and Manufacture, 43, 13, 1287- 1300, 2003.
• Abellan-Nebot J.V., Subirón F.R., A review of machining monitoring systems based on artificial intelligence process models, The International Journal of Advanced Manufacturing Technology, 47, 1-4, 237- 257, 2010.
• Rajasekaran T., Palanikumar K., Vinayagam B.K., Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool, Production Engineering, 5, 2, 191-199, 2011.
• Azmi A.I., Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites, Advances in Engineering Software, vol. 82, pp. 53-64, 2015.
• Soori M., Arezoo B., Habibi M., Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system, International Journal of Computer Applications in Technology, 55, 4, 308-321, 2017.
• Santhanakrishnan M., Sivasakthivel P.S., Sudhakaran R., Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39, 2, 487-496, 2017.
• Kumar S., Singh R., Batish A., Singh T.P., Modeling the tool wear rate in powder mixed electrodischarge machining of titanium alloys using dimensional analysis of cryogenically treated electrodes and workpiece, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 231, 2, 271-282, 2017.
• Kumaran S.T., Ko T.J., Kurniawan R., Li C., Uthayakumar M., ANFIS modeling of surface roughness in abrasive waterjet machining of carbon fiber reinforced plastics, Journal of Mechanical Science and Technology, 31, 8, 3949-3954, 2017.
• Marzban M.A., Hemmati S.J., Modeling of abrasive flow rotary machining process by artificial neural network, The International Journal of Advanced Manufacturing Technology, 89, 1-4, 125-132, 2017.
• Chakraborty S., Das P.P., Kumar V., Application of grey-fuzzy logic technique for parametric optimization of non-traditional machining processes, Grey Systems: Theory and Application, 8, 1, 46-68, 2018.
• Sokołowski A., On some aspects of fuzzy logic application in machine monitoring and diagnostics, Engineering Applications of Artificial Intelligence, 17, 4, 429-437, 2004.
• Peres C.R., Guerra R.E.H., Haber R.H., Alique A., Ros S., Fuzzy model and hierarchical fuzzy control integration: an approach for milling process optimization, Computers in Industry, 39, 3, 199-207, 1999.
• Dweiri F., Al-Jarrah M., Al-Wedyan H., Fuzzy surface roughness modeling of CNC down milling of Alumic-79, Journal of Materials Processing Technology, 133(3), 266-275, 2003.
• Kovac P., Rodic D., Pucovsky V., Savkovic B., Gostimirovic M., Application of fuzzy logic and regression analysis for modeling surface roughness in face milling, Journal of Intelligent Manufacturing, 24, 4, 755-762, 2013.
• Ramesh S., Karunamoorthy L., Palanikumar K., Fuzzy modeling and analysis of machining parameters in machining titanium alloy, Materials and Manufacturing Processes, 23, 4, 439-447, 2008.
• Ren Q., Balazinski M., Jemielniak K., Baron L., Achiche S., Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling, Soft Computing, 17, 9, 1687-1697, 2013.
• Barzani M.M., Zalnezhad E., Sarhan A.A., Farahany S., Ramesh S., Fuzzy logic based model for predicting surface roughness of machined Al-Si-Cu-Fe die casting alloy using different additives-turning, Measurement, 61, 150-161, 2015.
• Chakraborty S., Das P.P., Fuzzy Modelling and Parametric Analysis of the Ring Spinning Process, Tekstil ve Mu¨hendis, 26, 114, 132-148, 2019.
• Zadeh L., Fuzzy sets, International Journal of information and Control, 8, 3, 338-353, 1965.
• Cherkassky V., Mulier F., Learning from data: concepts, theory, and methods, USA: Wiley, 1998.
• Mamdani E.H., Assilian S., An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, 7, 1, 1-13, 1975.
• King P.J., Mamdani E.H., The application of fuzzy control systems to industrial processes, Automatica, 13, 3, 235-242, 1977.
• Kandpal B.C., Kumar J., Singh H., Optimization and characterization of EDM of AA 6061/10% Al2O3 AMMC using Taguchi's approach and utility concept, Production & Manufacturing Research, 5, 1, 351-370, 2017.
• Chakraborty S., Das P.P., Kumar V., A grey fuzzy logic approach for cotton fibre selection, Journal of The Institution of Engineers (India): Series E, 98, 1, 1-9, 2017.
• Rao S.R., Padmanabhan G., Parametric optimization in electrochemical machining using utility based taguchi method, Journal of Engineering Science and Technology, 10, 1, 81-96, 2015.

Identyfikatory

DOI

10.24425/mper.2019.130504