nach oben

Journal of Intelligent Manufacturing

Erschienen in:

12.02.2015

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

verfasst von: Kumar Abhishek, V. Rakesh Kumar, Saurav Datta, Siba Sankar Mahapatra

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 8/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR \((\hbox {R}_{\mathrm{a}})\) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.

Nächster Artikel Digital description of products, processes and resources for task-oriented programming of assembly systems

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001–1014.CrossRef

Bachlaus, M., Pandey, M. K., Mahajan, C., Shankar, R., & Tiwari, M. K. (2008). Designing an integrated multi-echelon agile supply chain network: A hybrid Taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing, 19(6), 747–761.CrossRef

Bagci, E., & Işık, B. (2006). Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN. International Journal of Advance Manufacturing Technology, 31, 10–17.CrossRef

Bharathi, R. S., & Baskar, N. (2011). Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation. International Journal of Advanced Manufacturing Technology, 54(5–8), 445–463.

Chatterjee, S., Abhishek, K., Yadav, R. K., & Mahapatra, S. (2014). Optimization of drilling process parameters by harmony search algorithm. In Recent advances and innovations in engineering (ICRAIE), IEEE (pp. 1–5).

Chaube, A., Benyoucef, L., & Tiwari, M. K. (2012). An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. Journal of Intelligent Manufacturing, 23(4), 1141–1155.CrossRef

Chen, H., Lin, J., Yang, Y., & Tsai, C. (2010). Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach. Expert System Application, 37(10), 7147–7153.CrossRef

Crepinsek, M., & Liu, S.-H. (2012). A note on teaching–learning-based optimization algorithm. Information Sciences, 212, 79–93.CrossRef

Cus, F., Balic, J., & Zu Perl, U. (2009). Hybrid ANFIS-ants system based optimization of turning parameters. Journal of Achievements in Materials and Manufacturing Engineering, 36(1), 79–86.

Duran, O., Rodriguez, R., & Consalter, L. A. (2008). PSO for selecting cutting tools geometry. In Lecture notes in computer science (Vol. 5271, pp. 265–272).

Farahnakian, M., Razfar, M. R., Moghri, M., & Asadnia, M. (2011). The selection of milling parameters by the PSO-based neural network modeling method. International Journal of Advanced Manufacturing Technology, 57(1–4), 49–60.CrossRef

Ferreira, J. R., Coppini, N. L., & Neto, F. L. (2001). Characteristics of carbon–carbon composite turning. Journal of Materials Processing Technology, 109, 65–71.CrossRef

Gaitonde, V. N., & Karnik, S. R. (2012). Minimizing burr size in drilling using artificial neural network (ANN)–particle swarm optimization (PSO) approach. Journal of Intelligent Manufacturing, 23(5), 1783–1793.CrossRef

Gao, Q., Zhang, Q., Su, S., & Zhang, J. (2008). Parameter optimization model in electrical discharge machining process. Journal of Zhejiang University Science A, 9(1), 104–108.CrossRef

Gonzalez-Alvarez, D. L., Vega-Rodríguez, M. A., Gomez-Pulido, J. A., & Sánchez-Pérez, J. M. (2012). Multiobjective teaching–learning-based optimization (MO-TLBO) for Motif finding. In 13th IEEE international symposium on computational intelligence and informatics, Budapest, Hungary.

Gupta, M., & Gill, S. K. (2012). Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing. Frontiers of Mechanical Engineering, 7(4), 417–426.CrossRef

Jain, N. K., & Jain, V. K. (2007). Optimization of electro-chemical machining process parameters using genetic algorithms. Machining Science and Technology, 11(2), 235–258.CrossRef

Jayabal, S., & Natarajan, U. (2010). Optimization of thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites using nelder-mead and genetic algorithm methods. International Journal of Advanced Manufacturing Technology, 51(1–4), 371–381.CrossRef

Kadirgama, K., Noor, M. M., & Alla, A. N. A. (2010). Response ant colony optimization of end milling surface roughness. Sensors, 10, 2054–2063.CrossRef

Khan, M. Adam, Senthil Kumar, A., & Poomari, A. (2012). A hybrid algorithm to optimize cutting parameter for machining GFRP composite using alumina cutting tools. International Journal of Advance Manufacturing Technology, 59, 1047–1056.CrossRef

Kilickap, E., Huseyinoglu, M., & Yardimeden, A. (2011). Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. International Journal of Advanced Manufacturing Technology, 52(1–4), 79–88.CrossRef

Kim, K. S., Lee, D. G., & Kwak, Y. K. (1990). Cutting (milling) characteristics of carbon fiber/epoxy composites. Transactions of the Korean Society of Mechanical Engineers, 14(1), 237–42.

Kim, K. S., Lee, D. G., Kwak, Y. K., & Namgung, S. (1992). Machinability of carbon fiber-epoxy composite materials in turning. Journal of Materials Processing Technology, 32, 553–570.CrossRef

Kolahan, F., & Khajavi, A. H. (2009). A statistical approach for predicting and optimizing depth of cut in AWJ machining for 6063-T6 Al alloy. Proceedings of World Academy of Science, Engineering and Technology, 59, 142–145.

Koplev, A. (1980). Cutting of CFRP with single edge tools. In Proceeding of third international conference on composite materials, Paris (pp. 1597–1605).

Krishnasamy, U., & Nanjundappan, D. (2014). A refined teaching–learning based optimization algorithm for dynamic economic dispatch of integrated multiple fuel and wind power plants. Mathematical Problems in Engineering. doi:10.1155/2014/956405

Kumar, A., Bharaneeswaran, P., & Annamalai, R. (2012a). Experimental investigation of K20 carbide and PCD insert on machining GFRP composite. International Conference on Industrial and Intelligent Information, 31, 149–155.

Kumar, S., Gupta, M., Satsangi, P. S., & Sardana, H. K. (2012b). Cutting forces optimization in the turning of UD-GFRP composites under different cutting environment with polycrystalline diamond tool. International Journal of Engineering, Science and Technology, 4(2), 106–121.

Kumar, S., Meenu, & Satsangi, P. S. (2013). Multiple performance optimization in machining of UD-GFRP composites by a PCDtool using distance-based pareto genetic algorithm (DPGA). Mechanica Confab, 2(2), 49–66.

Kumar, V. V., Pandey, M. K., Tiwari, M. K., & Ben-Arieh, D. (2010). Simultaneous optimization of parts and operations sequences in SSMS: A chaos embedded Taguchi particle swarm optimization approach. Journal of Intelligent Manufacturing, 21(4), 335–353.

Lee, D. G., Kim, K. S., & Kwak, Y. K. (1991). Manufacturing of a SCARA type direct-drive robot with graphite fiber epoxy composite material. Robotica, 9, 219–229.CrossRef

Lee, D. G., Sin, H. C., & Suh, N. P. (1985). Manufacturing of a graphite epoxy composite spindle for a machine tool. CIRP Annals-Manufacturing Technology, 34(1), 365–369.CrossRef

Li, J. G., Yao, Y. X., Gao, D., Liu, C. Q., & Yuan, Z. J. (2008). Cutting parameters optimization by using particle swarm optimization (PSO). Applied Mechanics and Materials, 10–12, 879–883.CrossRef

Li, M. J., Soo, S. L., Aspinwall, D. K., Pearson, D., & Leahy, W. (2014). Influence of lay-up configuration and feed rate on surface integrity when drilling carbon fibre reinforced plastic (CFRP) composites. Procedia CIRP, 13, 399–404.CrossRef

Lin, L., Hao, X.-C., Gen, M., & Jo, J.-B. (2012). Network modeling and evolutionary optimization for scheduling in manufacturing. Journal of Intelligent Manufacturing, 23(6), 2237–2253.CrossRef

Lubin, G. (1982). Handbook of composites (pp. 625–629). New York: Van Nostrand Reinhold.CrossRef

Mahapatra, S. S., & Patnaik, A. (2007). Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. International Journal of Advanced Manufacturing Technology, 34(9–10), 911–925.CrossRef

Maji, K., & Pratihar, D. K. (2010). Modeling of electrical discharge machining process using conventional regression analysis and genetic algorithms. Journal of Materials Engineering and Performance, 20(7), 1121–1127.CrossRef

Mallick, P. K. (1988). Fiber-reinforced composites (pp. 3–4). New York: Dekker.

Mandal, D., Pal, S. K., & Saha, P. (2007). Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. Journal of Materials Processing Technology, 186(1–3), 154–162.CrossRef

Meenu, & Kumar, S. (2013). Prediction of surface roughness in turning of UDGFRP using mathematical model and simulated annealing. International Journal of Advances in Engineering and Technology, 4(2), 81–85.

Palanikumar, K., & Davim, J. P. (2007). Mathematical model to predict tool wear on the machining of glass fibre reinforced plastic composites. Materials & Design, 28, 2008–2014.CrossRef

Palanikumar, K., Karunamoorthy, L., Karthikeyan, R., & Latha, B. (2006). Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the Taguchi method with fuzzy logics. Metals and materials International, 12, 483–491.CrossRef

Palanikumar, K., Latha, B., Senthilkumar, V. S., & Karthikeyan, R. (2009). Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II). Metals and Materials International, 15(2), 249–258.CrossRef

Palanisamy, P., Rajendran, I., & Shanmugasundaram, S. (2007). Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. International Journal of Advanced Manufacturing Technology, 32(7–8), 644–655.CrossRef

Parent, L., Songmene, V., & Kenne, J. (2007). A generalised model for optimising an end milling operation. Production Planning and Control, 18(4), 319–337.CrossRef

Patel, V. K., & Savsani, V. J. (2014a). Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching–learning based optimization (MO-ITLBO) algorithm. Chemical Engineering Research and Design, 92, 2371–2382.CrossRef

Patel, V. K., & Savsani, V. J. (2014b). A multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Information Sciences. doi:10.1016/j.ins.2014.05.049

Pecat, O., Rentsch, R., & Brinksmeier, E. (2012). Influence of milling process parameters on the surface integrity of CFRP. In 5th CIRP conference on high performance cutting (pp. 466–470).

Prasad, C. F., Jayabal, S., & Natarajan, U. (2007). Optimization of tool wear in turning using genetic algorithm. Indian Journal of Engineering and Materials Sciences, 14(6), 403–407.

Rajasekaran, T., Palanikumar, K., & Vinayagam, B. K. (2011). Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Production Engineering Research and Development, 5, 191–199.CrossRef

Rajasekaran, T., Palanikumar, K., & Vinayagam, B. K. (2012). Turning CFRP composites with ceramic tool for surface roughness analysis. Procedia Engineering, 38, 2922–2929.CrossRef

Rao, R. V., & Kalyankar, V. D. (2011). Parameters optimization of advanced machining processes using TLBO algorithm. Singapore: EPPM.

Rao, R. V., & Kalyankar, V. D. (2013a). Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm. Scientia Iranica E, 20(3), 967–974.

Rao, R. V., & Kalyankar, V. D. (2013b). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 524–531.CrossRef

Rao, R. V., & Patel, V. (2012). An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3, 535–560.CrossRef

Rao, R. V., & Patel, V. (2013a). An improved teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica D, 20(3), 710–720.

Rao, R. V., & Patel, V. (2013b). Comparative performance of an elitist teaching–learning-based optimization algorithm for solving unconstrained optimization problems. International Journal of Industrial Engineering Computations, 4, 29–50.

Rao, R. V., Pawar, P. J., & Shankar, R. (2008). Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(8), 949–958.CrossRef

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer Aided Design, 43, 303–315.CrossRef

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012a). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183, 1–15.CrossRef

Rao, R. V., Savsani, V. J., & Balic, J. (2012b). Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, 44(12), 1447–1462.

Rao, R. V., & Waghmare, G. G. (2013). Solving composite test functions using teaching–learning-based optimization algorithm. In Proceedings of the international conference on Frontiers of intelligent computing: Theory and applications (FICTA) advances in intelligent systems and computing (Vol. 199, pp. 395–403).

Rao, R. V., & Waghmare, G. G. (2014a). A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions. Journal of King Saud University—Computer and Information Sciences, 26, 332–346.CrossRef

Rao, R. V., & Waghmare, G. G. (2014b). Complex constrained design optimisation using an elitist teaching–learning-based optimisation algorithm. International Journal of Metaheuristics, 3(1), 81–102.CrossRef

Reugg, C., & Habermeir, J. (1980). Composite propeller shafts design and optimization. In A. Bunsell, et al. (Eds.), Advances in composite material (Proceedings of ICCM 3) (Vol. 2, pp. 1740–1755). Paris: Pergamon Press.CrossRef

Sait, A. N. (2010). Optimization of machining parameters of GFRP pipes using evolutionary techniques. International Journal of Precision Engineering and Manufacturing, 11(6), 891–900.CrossRef

Sait, A. N., Aravindan, S., & Haq, A. N. (2009). Optimisation of machining parameters of glass-fibre-reinforced plastic (GFRP) pipes by desirability function analysis using Taguchi technique. International Journal of Advance Manufacturing Technology, 43, 581–589.CrossRef

Santhanakrishnam, G., Krishnamurthy, R., & Malhotra, S. K. (1988). Machinability characteristics of fiber reinforced plastics composites. Journal of Mechanical Working Technology, 17, 195–204.

Satapathy, S. C., & Naik, A. (2011). Data clustering based on teaching–learning-based optimization. In Evolutionary and memetic computing lecture notes in computer science, (Vol. 7077, pp. 148–156).

Satapathy, S. C., Naik, A., & Parvathi, K. (2013a). A teaching learning based optimization based on orthogonal design for solving global optimization problems. Springer Plus, 2(130), 1–12.

Satapathy, S. C., Naik, A., & Parvathi, K. (2013b). Weighted teaching–learning-based optimization for global function optimization. Applied Mathematics, 4, 429–439.CrossRef

Satishkumar, S., & Asokan, P. (2008). Selection of optimal conditions for CNC multitool drilling system using non-traditional techniques. International Journal of Machining and Machinability of Materials, 3(1–2), 190–207.CrossRef

Schwartz, M. M. (1984). Composite materials handbook, Ch. 7 (p. 7). New York: McGraw-Hill.

Thirumalai, R., & Senthilkumaar, J. S. (2013). Multi-criteria decision making in the selection of machining parameters for Inconel 718. Journal of Mechanical Science and Technology, 27(4), 1109–1116.CrossRef

Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M., & Razmi, J. (2012). Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing, 23(3), 759–774.CrossRef

Verma, R. K., Abhishek, K., Datta, S., & Mahaptra, S. S. (2011). Fuzzy rule based optimization in machining of FRP composites. Turkish Journal of Fuzzy Systems, 2(2), 99–121.

Vijayakumar, K., Prabhaharan, G., Asokan, P., & Saravanan, R. (2003). Optimization of multi-pass turning operations using ant colony system. International Journal of Machine Tools and Manufacture, 43(15), 1633–1639.CrossRef

Xi, J., & Liao, G. (2009). Cutting parameter optimization based on particle swarm optimization. In 2nd International conference on intelligent computing technology and automation, ICICTA 2009 (Vol. 1, pp. 255–258).

Xu, F., Zhu, J. J., Wu, X., Zang, X. J., & Zuo, D. W. (2010). Parameter optimization of milling Ti6Al4V using GA approach. Key Engineering Materials, 426–427, 1–4.CrossRef

Yang, S., Srinivas, J., Mohan, S., Lee, D.-M., & Balaji, S. (2009). Optimization of electric discharge machining using simulated annealing. Journal of Materials Processing Technology, 209(9), 4471–4475.CrossRef

Yang, X. S. (2009). Harmony search as a metaheuristic algorithm. In Z. W. Geem (Ed.), Music inspired harmony search algorithm: Theory and applications, studies in computational intelligence (Vol. 191, pp. 1–14). Berlin: Springer.CrossRef

Zain, A. M., Haron, H., & Sharif, S. (2010). Simulated annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling Ti–6Al–4V. Machining Science and Technology, 14, 43–62.CrossRef

Zou, F., Wang, L., Hei, X., Chen, D., & Wang, B. (2013). Multi-objective optimization using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(4), 1291–1300.CrossRef

Titel: Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)
verfasst von: Kumar Abhishek
V. Rakesh Kumar
Saurav Datta
Siba Sankar Mahapatra
Publikationsdatum: 12.02.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 8/2017
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-015-1050-8

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 8/2017

A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition

Correlation-aware QoS modeling and manufacturing cloud service composition

Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes

A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

Embedding ant system in genetic algorithm for re-entrant hybrid flow shop scheduling problems with time window constraints

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

Premium Partner

Marktübersichten