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Journal of Intelligent Manufacturing

Erschienen in:

18.11.2015

Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach

verfasst von: Jagadish, Sumit Bhowmik, Amitava Ray

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 8/2019

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Abstract

The aim of this paper is to process modelling of AWJM process on machining of green composites using fuzzy logic (FL). An integrated expert system comprising of Takagi–Sugeno–Kang (TSK) fuzzy model with subtractive clustering (SC) has been developed for prediction surface roughness in green AWJM. Initially, the data base is generated by performing the experiments on AWJM process using Taguchi \((\hbox {L}_{27})\) orthogonal array. Thereafter, SC is used to extracts the cluster information which are then utilized to construct the TSK model that best fit the data using minimum rules. The performance of TSK–FL model has been tested for its accuracy in prediction of surface roughness in AWJM process using artificially generated test cases. The result shows that, predictions through TSK–FL model are comparable with experimental results. The developed model can be used as systematic approach for prediction of surface roughness in green manufacturing processes.

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Abdul Khalil, H. P. S., Bhatt, A. H., & Yusra, A. F. I. (2012). Green composites from sustainable cellulose nanofibrils: A review. Carbohydrate Polymers, 87, 963–979.

Akkurt, A., Kulekci, M. K., Seker, U., & Ercan, F. (2004). Effect of feed rate on surface roughness in abrasive waterjet cutting applications. Journal of Materials Processing Technology, 147, 389–396.

Azmir, M. A., & Ahsan, A. K. (2008). Investigation on glass/epoxy composite surfaces machined by abrasive water jet machining. Journal of Materials Processing Technology, 198, 122–128.

Benedict, G. F. (1987). Non-traditional manufacturing processes (pp. 2–3, 67–86). New York: Marcel Decker Inc.

Bortolan, G., & Degami, R. (1985). A review of some methods for ranking fuzzy subset. Fuzzy Sets and Systems, 15(1), 1–19.

Bradford, J. D., & Richardson, D. B. (1980). Production engineering technology (3rd ed., pp. 74–93). London: Macmillan.

Caydas, U., & Hascalik, A. (2008). A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. Journal of Materials Processing Technology, 202, 574–582.

Chakravarthy, P. S., & Ramesh Babu, N. (1999). New approach for selection of optimal process parameters in abrasive water jet cutting. Materials and Manufacturing Processes, 14(4), 581–600.

Chandramohan, D. (2014). Studies on natural fiber particle reinforced composite material for conservation of natural resources. Advances in Applied Science Research, 5(2), 305–315.

Chen, F. L., Siores, E., & Patel, K. (2002). Improving the cut surface qualities using different controlled nozzle oscillation techniques. International Journal of Machine Tools and Manufacture, 42, 717–722.

Chen, L., Siores, E., & Wong, W. C. K. (1995). Kerf characteristics in abrasive waterjet cutting of ceramic materials. International Journal of Machine Tools and Manufacture, 36(11), 1201–1206.

Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2(3), 267–278.

Choi, A. C. K., Kaebernick, H., & Lai, W. H. (1997). Manufacturing process modeling for environmental impact assessment. Journal of Materials Processing Technology, 70(1–3), 231–238.

Cojbasica, Z., Petkovica, D., Shamshirband, S., Tongc, C. W., Ch, S., Jankovica, P., et al. (2015). Surface roughness prediction by extreme learning machineconstructed with abrasive water jet. Precision Engineering. doi:10.1016/j.precisioneng.2015.06.013.

Deris, A. M., Zain, A. M., & Sallehuddin, R. (2013). Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining. Meccanica, 48, 1937–1945.

Gachter, R., & Muller, H. (1990). Plastics additives (3rd ed.). Munich: Hanser Publishers.

Georgios, K., Arlindo, S., & Mihail, F. (2013). Green composites: A review of adequate materials for automotive applications. Composites Part B: Engineering, 44, 120–127.

Haman, A., & Geogranas, N. D. (2008). Comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In IEEE international workshop on haptic audio visual environments and their applications, Ottawa-Canada, October 18–19, 2008.

Hascalik, A., Caydas, U., & Gurun, H. (2007). Effect of traverse speed on abrasive waterjet machining of Ti–6Al–4V alloy. Materials and Design, 28, 1953–1957.

Hashish, M. (1991). Advances in composite machining with abrasive-waterjets. ASME Process Manufacturing Computer Materials, 49(27), 93–111.

Jegaraj, J. J. R., & Babu, N. R. (2005). A strategy for efficient and quality cutting of materials with abrasive waterjets considering the variation in orifice and focusing nozzle diameter. International Journal of Machine Tools and Manufacture, 45, 1443–1450.

Karakurt, L., Aydin, G., & Aydiner, K. (2012). An experimental study on the depth of cut of granite in abrasive waterjet cutting. Materials and Manufacturing Processes, 27, 538–544.

Kok, M., Kanca, E., & Eyercioglu, O. (2011). Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming. The International Journal of Advanced Manufacturing Technology, 55, 955–968.

Komanduri, R., Zhang, B., & Vissa, C. M. (1991). Machining of fibre reinforced composites. ASME Process Manufacturing Computer Materials, 49(27), 1–36.

Konig, W., & Rummenholler, S. (1993). Technological and industrial safety aspects in milling FRP. ASME Machining Advance Computer, 45(66), 1–14.

La-Mantia, F. P., & Morreale, M. (2011). Green composites: A brief review. Composites Part A: Applied Science and Manufacturing, 42, 579–588.

Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7, 1–13.

Markarian, J. (2002). Additive developments aid growth in wood–plastic composites. Plastics Additives and Compounding, 4, 18–21.

MATLAB. (2006). Fuzzy logic toolbox, user’s guide. Natick: The MathWorks Inc.

Minitab14. (2003). Minitab user manual release 14. State College, PA, USA.

Momber, A. W., & Kovacevic, R. (1992). Principles of abrasive water jet machining (1st ed., pp. 214–225). London: Springer.

Nair, K. C. M., Kumar, R. P., Thomas, S., Schit, S. C., & Ramamurthy, K. (2000). Rheological behavior of short sisal fiber-reinforced polystyrene composites. Composites Part A: Applied Science and Manufacturing, 31, 1231–1240.

Nareshbabu, M., & Muthukrishnan, N. (2014). Investigation on surface roughness in abrasive water-jet machining by the response surface method. Materials and Manufacturing Processes, 29, 1422–1428.

Olsen, J. H. (2008). Green cutting with waterjets, Waterjet Cutting Articles, a publication of the fabricators and manufacturer association. International Rockford.

Parikh, P. J., & Lam, S. S. (2009). Parameter estimation for abrasive water jet machining process using neural networks. The International Journal of Advanced Manufacturing Technology, 40, 497–502.

Prabhu, S., Uma, M., & Vinayagam, B. K. (2015). Surface roughness prediction using Taguchi-fuzzy logic-neural for network analysis for CNT nanofluids based grinding process. Neural Computer and Application, 26, 41–55.

Pritchard, G. (2004). Two technologies merge: Wood–plastic composites. Plastics Additives and Compounding, 6, 18–21.

Raj, R. B., Kokta, B. V., Dembele, F., & Sanschagrain, B. (1989). Compounding of cellulose fibers with polypropylene: Effect of fiber treatment on dispersion in the polymer matrix. Journal of Applied Polymer Science, 38, 1987–1996.

Ramkumar, J., Malhotra, S. K., & Krishnamurthy, R. (2004). Effect of workpiece vibration on drilling of GFRP laminates. Journal of Materials Processing Technology, 152, 329–332.

Ramulu, M., & Arola, D. (1994). The influence of abrasive water jet cutting conditions on the surface quality of graphite/epoxy laminates. International Journal of Machine Tools and Manufacture, 34(3), 295–313.

Rojas, R. (1996). Neural networks. Berlin: Springer.

Sevil, E. H., & Oysal, Y. (2015). Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network. Journal of Intelligent Manufacturing, 26, 403–413.

Shabgarda, M. R., Badamchizadehb, M. A., Ranjbarya, G., & Amini, K. (2013). Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes. Journal of Manufacturing Systems, 32, 32–39.

Sivapirakasam, S. P., Mathew, J., & Surianarayanan, M. (2011). Multi-attribute decision making for green electrical discharge machining. Expert Systems with Applications, 38(7), 8370–8374.

Sugeno, M. (1985). Industrial applications of fuzzy control. Amsterdam: Elsevier.

Todkar, M., & Patkure, J. (2014). Fuzzy modelling and Ga optimization for optimal selection of process parameters to maximize MRR in abrasive water jet machining. International Journal on Theoretical and Applied Research in Mechanical Engineering, 3(1), 9–16.

Vundavilli, P. R., Parappagoudar, M. B., Kodali, S. P., & Benguluri, S. (2012). Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process. Knowledge Based System, 27, 456–464.

Weller, E. J. (1984). Non-traditional machining processes (pp. 15–71). Dearborn: SME.

Yusup, N., Sarkheyli, A., Zain, A., Hashim, S. Z. M., & Ithnin, N. (2014). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, 25, 1463–1472.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

Zampaloni, M., Pourboghrat, F., & Yankovich, S. A. (2007). Kenaf natural fiber: A discussion on manufacturing problems and solutions. Composites Part A: Applied Science and Manufacturing, 38(6), 1569–1580.

Titel: Prediction of surface roughness quality of green abrasive water jet machining: a soft computing approach
verfasst von: Jagadish
Sumit Bhowmik
Amitava Ray
Publikationsdatum: 18.11.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 8/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-015-1169-7

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