nach oben

Journal of Intelligent Manufacturing

Erschienen in:

03.09.2018

Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2020

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Polycrystalline diamond (PCD) is increasingly becomes an important material used in the industry for cutting tools of difficult-to-machine materials due to its excellent characteristics such as hardness, toughness and wear resistance. However, its applications are restricted because of the PCD material is difficult to machine. Therefore, electrical discharge machining (EDM) is an ideal method suitable for PCD materials due to its non-contact process nature. The performance of EDM, however, is significantly influenced by its process parameters and type of electrode. In this study, soft computing technique was utilized to optimize the performance of the EDM in roughing condition for eroding PCD with copper tungsten or copper nickel electrode. Central composite design with five levels of three machining parameters viz. peak current, pulse interval and pulse duration has been used to design the experimental matrix. The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). It was found that copper tungsten electrode gave lower EWR, in comparison with the copper nickel electrode. The predictive model of radial basis function neural network (RBFNN) was developed to predict the MRR and EWR of the EDM process. The prominent predictive ability of RBFNN was confirmed as the prediction errors in terms of mean-squared error were found within the range of 6.47E−05 to 7.29E−06. Response surface plot was drawn to study the influences of machining parameters of EDM for shaping PCD with copper tungsten and copper nickel. Subsequently, moth search algorithm (MSA) was used to determine the optimal machining parameters, such that the MRR was maximized and EWR was minimized. Based on the obtained optimal parameters, confirmation test with the absolute error within the range of 1.41E−06 to 5.10E−05 validated the optimization capability of MSA.

Vorheriger Artikel Point-by-point prediction of cutting force in 3-axis CNC milling machines through voxel framework in digital manufacturing

Nächster Artikel A data-driven approach for constructing the component-failure mode matrix for FMEA

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

Abhishek, K., Rakesh Kumar, V., Datta, S., & Mahapatra, S. S. (2017). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm). Journal of Intelligent Manufacturing,28(8), 1769–1785. https://doi.org/10.1007/s10845-015-1050-8.CrossRef

Ahmad, M. H., Rahim, M. Z., Fauzi, M. M., Abdullah, A., Omar, Z., Ding, S., et al. (2018). Catalytic aided electrical discharge machining of polycrystalline diamond-parameter analysis of finishing condition. In IOP conference series: Materials science and engineering (Vols. 1, 295, pp. 012042). IOP Publishing.

Aich, U., & Banerjee, S. (2016). Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization. Applied Soft Computing,39, 64–83. https://doi.org/10.1016/j.asoc.2015.11.002.CrossRef

Alajmi, M. S., Alfares, F. S., & Alfares, M. S. (2017). Selection of optimal conditions in the surface grinding process using the quantum based optimisation method. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1326-2. (in Press).CrossRef

Alavi, F., & Jahan, M. P. (2017). Optimization of process parameters in micro-EDM of Ti–6Al–4V based on full factorial design. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-017-0103-x.CrossRef

Chakraborty, S., Chakraborty, R., Nagendrababu, K., Talla, G., & Gangopadhyay, S. (2016). Multi-response optimisation of surface texturing using artificial bee colony algorithm. International Journal of Mechatronics and Manufacturing Systems,9(3), 272–295. https://doi.org/10.1504/IJMMS.2016.079594.CrossRef

Dey, A., Debnath, M., & Pandey, K. M. (2017). Analysis of effect of machining parameters during electrical discharge machining using Taguchi-based multi-objective PSO. International Journal of Computational Intelligence and Applications,16(02), 1750010. https://doi.org/10.1142/S1469026817500109.CrossRef

Gopu, P., Dev Anand, M., & Rajesh, R. (2016). Application of regression analysis and taguchi method for prognostication and optimization of EDM process. International Journal of Control Theory and Applications,9(7), 3181–3191.

Huang, C. H., Yang, A. B., & Hsu, C. Y. (2018a). The optimization of micro EDM milling of Ti–6Al–4V using a grey Taguchi method and its improvement by electrode coating. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-1841-0. (in Press).CrossRef

Huang, C. H., Yang, A. B., & Hsu, C. Y. (2018b). The optimization of micro EDM milling of Ti–6Al–4V using a grey Taguchi method and its improvement by electrode coating. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-1841-0.CrossRef

Jagadish, & Ray, A. (2016). Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA). International Journal of Advanced Manufacturing Technology,87(5–8), 1299–1311. https://doi.org/10.1007/s00170-014-6372-8.CrossRef

Jahan, M. P. (2015). Electrical discharge machining (EDM): Types, technologies and applications. Hauppauge: Nova Science Publishers, Incorporated.

Jia, Y., Kim, B. S., Hu, D. J., & Ni, J. (2009). Parametric study on near-dry wire electrodischarge machining of polycrystalline diamond-coated tungsten carbide material. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture,224(2), 185–193. https://doi.org/10.1243/09544054JEM1602.CrossRef

Joshi, S., Govindan, P., Malshe, A., & Rajurkar, K. (2011). Experimental characterization of dry EDM performed in a pulsating magnetic field. CIRP Annals,60(1), 239–242. https://doi.org/10.1016/j.cirp.2011.03.114.CrossRef

Joshi, S. N., & Pande, S. S. (2011). Intelligent process modeling and optimization of die-sinking electric discharge machining. Applied Soft Computing,11(2), 2743–2755. https://doi.org/10.1016/j.asoc.2010.11.005.CrossRef

Karagöz, S., & Yıldız, A. R. (2017). A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. International Journal of Vehicle Design,73(1–3), 179–188.CrossRef

Klocke, F., Schwade, M., Klink, A., & Veselovac, D. (2013). Analysis of material removal rate and electrode wear in sinking EDM roughing strategies using different graphite grades. Procedia CIRP,6, 163–167.CrossRef

Kumar, P., & Parkash, R. (2016). Experimental investigation and optimization of EDM process parameters for machining of aluminum boron carbide (Al–B₄C) composite. Machining Science and Technology,20(2), 330–348. https://doi.org/10.1080/10910344.2016.1168931.CrossRef

Lee, H. T., Tai, T. Y., Liu, C., Hsu, F. C., & Hsu, J. M. (2011). Effect of material physical properties on residual stress measurement by EDM hole-drilling method. Journal of Engineering Materials and Technology, Transactions of the ASME,133(2), 021014. https://doi.org/10.1115/1.4000219.CrossRef

Lee, S., & Li, X. (2001). Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide. Journal of Materials Processing Technology,115(3), 344–358.CrossRef

Li, C., Li, L., Tang, Y., Zhu, Y., & Li, L. (2016a). A comprehensive approach to parameters optimization of energy-aware CNC milling. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1233-y. (in Press).CrossRef

Li, G., Rahim, M. Z., Ding, S., Sun, S., & Mo, J. (2016b). Experimental study on quality of PCD tools machined by different electric discharge grinding processes. Cogent Engineering,3(1), 1. https://doi.org/10.1080/23311916.2016.1228234.CrossRef

Li, G., Yi, S., Sun, S., & Ding, S. (2017). Wear mechanisms and performance of abrasively ground polycrystalline diamond tools of different diamond grains in machining titanium alloy. Journal of Manufacturing Processes,29, 320–331. https://doi.org/10.1016/j.jmapro.2017.08.010.CrossRef

Lin, M. Y., Tsao, C. C., Huang, H. H., Wu, C. Y., & Hsu, C. Y. (2015). Use of the grey-Taguchi method to optimise the micro-electrical discharge machining (micro-EDM) of Ti–6Al–4V alloy. International Journal of Computer Integrated Manufacturing,28(6), 569–576. https://doi.org/10.1080/0951192X.2014.880946.CrossRef

Lin, Y.-C., & Lee, H.-S. (2008). Machining characteristics of magnetic force-assisted EDM. International Journal of Machine Tools and Manufacture,48(11), 1179–1186. https://doi.org/10.1016/j.ijmachtools.2008.04.004.CrossRef

Liu, Z., Li, X., Wu, D., Qian, Z., Feng, P., & Rong, Y. (2018). The development of a hybrid firefly algorithm for multi-pass grinding process optimization. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1405-z. (in Press).CrossRef

Mahardika, M., Prihandana, G. S., Endo, T., Tsujimoto, T., Matsumoto, N., Arifvianto, B., et al. (2012). The parameters evaluation and optimization of polycrystalline diamond micro-electrodischarge machining assisted by electrode tool vibration. International Journal of Advanced Manufacturing Technology,60(9–12), 985–993. https://doi.org/10.1007/s00170-011-3674-y.CrossRef

Maity, K., & Mishra, H. (2016). ANN modelling and Elitist teaching learning approach for multi-objective optimization of μ-EDM. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1193-2. (in Press).CrossRef

Markopoulos, A. P., Manolakos, D. E., & Vaxevanidis, N. M. (2008). Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing,19(3), 283–292. https://doi.org/10.1007/s10845-008-0081-9.CrossRef

Mathworks, U. (2016). Matlab (p. 488). Natick, MA: The MathWorks, Inc.

McGeough, J. A. (1988). Advanced methods of machining. Berlin: Springer.

Meena, V. K., Azad, M. S., Singh, S., & Singh, N. (2017). Micro-EDM multiple parameter optimization for Cp titanium. International Journal of Advanced Manufacturing Technology,89(1–4), 897–904. https://doi.org/10.1007/s00170-016-9130-2.CrossRef

Mohanty, C. P., Mahapatra, S. S., & Sahu, J. (2016a). Parametric optimisation of electrical discharge machining process: A numerical approach. International Journal of Industrial and Systems Engineering,22(2), 207–244. https://doi.org/10.1504/IJISE.2016.073963.CrossRef

Mohanty, C. P., Mahapatra, S. S., & Singh, M. R. (2016b). A particle swarm approach for multi-objective optimization of electrical discharge machining process. Journal of Intelligent Manufacturing,27(6), 1171–1190. https://doi.org/10.1007/s10845-014-0942-3.CrossRef

Mohanty, C. P., Mahapatra, S. S., & Singh, M. R. (2017). An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm. Engineering Science and Technology, an International Journal,20(2), 552–562. https://doi.org/10.1016/j.jestch.2016.07.003.CrossRef

Mohanty, C. P., Satpathy, M. P., Mahapatra, S. S., & Singh, M. R. (2018). Optimization of cryo-treated EDM variables using TOPSIS-based TLBO algorithm. Sadhana-Academy Proceedings in Engineering Sciences. https://doi.org/10.1007/s12046-018-0829-7.CrossRef

Mujumdar, S. S., Curreli, D., Kapoor, S. G., & Ruzic, D. (2015). Modeling of melt-pool formation and material removal in micro-electrodischarge machining. Journal of Manufacturing Science and Engineering, Transactions of the ASME,137(3), 031007. https://doi.org/10.1115/1.4029446.CrossRef

Ong, P., Ho, C. S., Chin, D. D. V. S., Sia, C. K., Ng, C. H., Wahab, M. S., et al. (2017). Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1365-8. (in Press).CrossRef

Panda, S., Mishra, D., Biswal, B. B., & Nanda, P. (2015). Optimization of multiple response characteristics of EDM process using taguchi-based grey relational analysis and modified PSO. Journal of Advanced Manufacturing Systems,14(3), 123–148. https://doi.org/10.1142/S0219686715500092.CrossRef

Pashazadeh, H., Gheisari, Y., & Hamedi, M. (2016). Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm. Journal of Intelligent Manufacturing,27(3), 549–559. https://doi.org/10.1007/s10845-014-0891-x.CrossRef

Patel, K. M., Pandey, P. M., & Rao, P. V. (2011). Study on machinabilty of Al₂O₃ ceramic composite in EDM using response surface methodology. Journal of Engineering Materials and Technology, Transactions of the ASME,133(2), 021004. https://doi.org/10.1115/1.4003100.CrossRef

Pellicer, N., Ciurana, J., & Delgado, J. (2011). Tool electrode geometry and process parameters influence on different feature geometry and surface quality in electrical discharge machining of AISI H13 steel. Journal of Intelligent Manufacturing,22(4), 575–584. https://doi.org/10.1007/s10845-009-0320-8.CrossRef

Priyadarshini, M., & Pal, K. (2016). Multi-objective optimisation of EDM process using hybrid Taguchi-based methodologies for Ti–6Al–4V alloy. International Journal of Manufacturing Research,11(2), 144–166. https://doi.org/10.1504/IJMR.2016.078246.CrossRef

Rahim, M. Z., Ding, S., & Mo, J. (2016a). Electrical discharge grinding of polycrystalline diamond—Effect of wheel rotation. Machining Science and Technology,20(1), 62–78. https://doi.org/10.1080/10910344.2015.1085315.CrossRef

Rahim, M. Z., Li, G., Ding, S., Mo, J., & Brandt, M. (2016b). Electrical discharge grinding versus abrasive grinding in polycrystalline diamond machining—tool quality and performance analysis. International Journal of Advanced Manufacturing Technology,85(1–4), 263–277. https://doi.org/10.1007/s00170-015-7935-z.CrossRef

Rajesh, R., Dev Anand, M., & Benny, K. N. (2017). Prediction of EDM process parameters for AISI 1020 steel using RSM, GRA and ANN. International Journal of Mechanical Engineering and Technology,8(5), 924–940.

Rao, R. V., Rai, D. P., & Balic, J. (2016). Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1210-5. (in Press).CrossRef

Sabareesaan, K. J., Varahamoorthi, R., Al Ani, H., & Jaya, J. (2015). Response surface methodology for optimizing EDM process parameters in machining of inconel X750 using copper electrode. International Journal of Applied Engineering Research,10(2), 5151–5163.

Saha, S. K., & Choudhury, S. (2009). Experimental investigation and empirical modeling of the dry electric discharge machining process. International Journal of Machine Tools and Manufacture,49(3–4), 297–308.CrossRef

Sarıkaya, M., & Yılmaz, V. (2016). Optimization and predictive modeling using S/N, RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel. Neural Computing and Applications. https://doi.org/10.1007/s00521-016-2775-9. (in Press).CrossRef

Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). A multi-performance prediction model based on ANFIS and new modified-GA for machining processes. Journal of Intelligent Manufacturing,26(4), 703–716. https://doi.org/10.1007/s10845-013-0828-9.CrossRef

Shabgard, M., Seydi, S., & Seyedzavvar, M. (2016). Novel approach towards finite element analysis of residual stresses in electrical discharge machining process. The International Journal of Advanced Manufacturing Technology,82(9), 1805–1814. https://doi.org/10.1007/s00170-015-7510-7.CrossRef

Shen, Y., Liu, Y., Dong, H., Zhang, K., Lv, L., Zhang, X., et al. (2017). Parameters optimization for sustainable machining of Ti–6Al–4V using a novel high-speed dry electrical discharge milling. The International Journal of Advanced Manufacturing Technology,90(9), 2733–2740. https://doi.org/10.1007/s00170-016-9600-6.CrossRef

Singh, N. K., Pandey, P. M., & Singh, K. K. (2017). Experimental investigations into the performance of EDM using argon gas-assisted perforated electrodes. Materials and Manufacturing Processes,32(9), 940–951. https://doi.org/10.1080/10426914.2016.1221079.CrossRef

Somashekhar, K. P., Ramachandran, N., & Mathew, J. (2010). Optimization of material removal rate in micro-EDM using artificial neural network and genetic algorithms. Materials and Manufacturing Processes,25(6), 467–475. https://doi.org/10.1080/10426910903365760.CrossRef

Tai, T. Y., & Nguyen, K. T. (2016). The grain size effect of polycrystalline diamond on surface integrity by using micro EDM. Procedia CIRP,42, 305–310. https://doi.org/10.1016/j.procir.2016.02.290.CrossRef

Tanjilul, M., Ahmed, A., Kumar, A. S., & Rahman, M. (2018). A study on EDM debris particle size and flushing mechanism for efficient debris removal in EDM-drilling of Inconel 718. Journal of Materials Processing Technology,255, 263–274. https://doi.org/10.1016/j.jmatprotec.2017.12.016.CrossRef

Tripathy, S., & Tripathy, D. K. (2017). Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis. Machining Science and Technology,21(3), 362–384. https://doi.org/10.1080/10910344.2017.1283957.CrossRef

Tsai, H., Yan, B., & Huang, F. (2003). EDM performance of Cr/Cu-based composite electrodes. International Journal of Machine Tools and Manufacture,43(3), 245–252.CrossRef

Wang, D., Zhao, W. S., Gu, L., & Kang, X. M. (2011). A study on micro-hole machining of polycrystalline diamond by micro-electrical discharge machining. Journal of Materials Processing Technology,211(1), 3–11. https://doi.org/10.1016/j.jmatprotec.2010.07.034.CrossRef

Wang, G.-G. (2016). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing. https://doi.org/10.1007/s12293-016-0212-3.CrossRef

Yan, J., Watanabe, K., & Aoyama, T. (2014a). Micro-electrical discharge machining of polycrystalline diamond using rotary cupronickel electrode. CIRP Annals-Manufacturing Technology,63(1), 209–212. https://doi.org/10.1016/j.cirp.2014.03.058.CrossRef

Yan, J., Watanabe, K., & Aoyama, T. (2014b). Micro-electrical discharge machining of polycrystalline diamond using rotary cupronickel electrode. CIRP Annals-Manufacturing Technology,63(1), 209–212.CrossRef

Yan, M.-T., Fang, G.-R., & Liu, Y.-T. (2013). An experimental study on micro wire-EDM of polycrystalline diamond using a novel pulse generator. The International Journal of Advanced Manufacturing Technology,66(9), 1633–1640. https://doi.org/10.1007/s00170-012-4446-z.CrossRef

Yanagida, D., Minami, H., & Watanabe, K. (2016). Electrical Discharge Machining of PCD in Ultrapure Water. In Procedia CIRP (Vol. 42, pp. 292–296). https://doi.org/10.1016/j.procir.2016.02.288.CrossRef

Yang, X.-S. (2010). Engineering optimization: An introduction with metaheuristic applications. Hoboken: Wiley.CrossRef

Yildiz, B. S., & Yildiz, A. R. (2017). Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes. Materialpruefung/Materials Testing,59(5), 425–429. https://doi.org/10.3139/120.111024.CrossRef

Zainal, N., Zain, A. M., Radzi, N. H. M., & Othman, M. R. (2016). Glowworm swarm optimization (GSO) for optimization of machining parameters. Journal of Intelligent Manufacturing,27(4), 797–804. https://doi.org/10.1007/s10845-014-0914-7.CrossRef

Zainuddin, Z., & Ong, P. (2011). Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network. Expert Systems with Applications,38(11), 13711–13722.

Titel: Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond
Publikationsdatum: 03.09.2018
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 1/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-018-1443-6

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 1/2020

An approach to design reconfigurable manufacturing tools to manage product variability: the mass customisation of eyewear

Point-by-point prediction of cutting force in 3-axis CNC milling machines through voxel framework in digital manufacturing

Literature review of Industry 4.0 and related technologies

Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study

Retraction Note to: Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique

Service manufacturing = Process-as-a-Service + Manufacturing Operations-as-a-Service

Premium Partner

Marktübersichten