Investigating electrochemical drilling (ECD) using statistical and soft computing techniques

Tajdari, Mehdi; Zare Chavoshi, Saeed

doi:10.22061/jcarme.2015.275

تعداد نشریات	11
تعداد شماره‌ها	228
تعداد مقالات	2,321
تعداد مشاهده مقاله	3,640,276
تعداد دریافت فایل اصل مقاله	2,676,854

	Investigating electrochemical drilling (ECD) using statistical and soft computing techniques
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 7، دوره 4، شماره 2 - شماره پیاپی 8، فروردین 2015، صفحه 181-191 اصل مقاله (1.5 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2015.275
نویسندگان
Mehdi Tajdari^* ؛ Saeed Zare Chavoshi
Department of Mechanical Engineering, Islamic Azad University, Arak Branch, Arak, Iran
تاریخ دریافت: 02 آذر 1392، تاریخ بازنگری: 24 مرداد 1393، تاریخ پذیرش: 04 آبان 1393
چکیده
In the present study, five modeling approaches of RA, MLP, MNN, GFF, and CANFIS were applied so as to estimate the radial overcut values in electrochemical drilling process. For these models, four input variables, namely electrolyte concentration, voltage, initial machining gap, and tool feed rate, were selected. The developed models were evaluated in terms of their prediction capability with measured values. It was clearly seen that the proposed models were capable of predicting the radial overcut. However, the MLP model predicted the radial overcut with higher accuracy than the other models. The statistical analysis showed how much the radial overcut was mainly influenced by voltage and electrolyte concentration during the electrochemical drilling process.
کلیدواژه‌ها
Soft computing techniques؛ Statistical analysis؛ Radial overcut؛ ECD

مراجع
[1] H. El-Hofy, Advanced machining processes. McGraw-Hill, New York, (2005). [2] X. Fang, N. Qu, Y Zhang, Z. Xu and D. Zhu, “Improvement of hole exit accuracy in electrochemical drilling by applying a potential difference between an auxiliary electrode and the anode”, Journal of Materials Processing Technology, Vol. 214, No. 3, pp. 556-564, (2014) [3] W. Wang, D. Zhu, N. S. Qu, S. F. Huang and X. L. Fang, “Electrochemical drilling with vacuum extraction of electrolyte”, Journal of Materials Processing Technology, Vol. 210, No. 2, pp. 238-244, (2010). [4] D. Zhu, W. Wang, X. L. Fang, N. S. Qu and Z. Y. Xu, “Electrochemical drilling of multiple holes with electrolyte-extraction”, CIRP Annals - Manufacturing Technology, Vol. 59, No.1, pp. 239-242, (2010). [5] W. Wang, D. Zhu, N. S. Qu, S. F. Huang and X. L. Fang, “Electrochemical drilling inclined holes using wedged electrodes”, Int. J. Adv. Manuf. Technol., Vol. 47, No. 11, pp. 29-1136, (2010). [6] Z. W. Fan and L. W. Hourng, “Electrochemical micro-drilling of deep holes by rotational cathode tools”, Int. J. Adv. Manuf. Technol., Vol. 52, No. 5-8, pp. 555-563, (2011). [7] Z. W. Fan, L. W. Hourng and M. Y. Lin, “Experimental investigation on the influence of electrochemical micro-drilling by short pulsed voltage”, Int. J. Adv. Manuf. Technol., Vol. 61, No. 9-12, pp. 957-966, (2012). [8] M. A. H. Mithu, G. Fantoni and J. Ciampi, “The effect of high frequency and duty cycle in electrochemical microdrilling”, Int. J. Adv. Manuf. Technol., Vol. 55, No. 9-12, pp. 921-933, (2011). [9] H. P. Tsui, Hung, J. C. You and B. H. Yan, “Improvement of electrochemical microdrilling accuracy using helical tool”, Materials and Manufacturing Processes, Vol. 23, No. 5, pp. 499-505, (2008). [10] Y. Liu and S. F. Huang, “Experimental Study on Electrochemical Drilling of Micro Holes with High Aspect Ratio”, Advanced Materials Research, Vol. 941-944, pp. 1952-1955, (2014). [11] S. Zare Chavoshi, “Analysis and predictive modeling of performance parameters in electrochemical drilling process”, Int. J. Adv. Manuf. Technol., Vol. 53, No. 9-12, pp. 1081-1101, (2011). [12] S. Zare Chavoshi and M. Tajdari, “Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool”, Int. J. Mater Form, Vol. 3, No. 4, pp. 233-239, (2010). [13] M. R. Soleymani Yazdi and S. Zare Chavoshi, “Analysis and estimation of state variables in CNC face milling of AL6061”, Prod. Eng. Res. Devel., Vol. 4, No. 6, pp. 535-543, (2010). [14] V. S. Sharma et al, “Estimation of cutting forces and surface roughness for hard turning using neural networks”, J. Intell. Manuf., Vol. 19, No. 4, pp. 473-483, (2008). [15] B. R. Sarkar, B. Doloi and B. Bhattacharyya, “Parametric analysis on electrochemical discharge machining of silicon nitride ceramics”, Int. J. Adv. Manuf. Technol., Vol. 28, No. 9-10, pp. 873-881, (2006). [16] D. S. Bilgi et al, “Predicting radial overcut in deep holes drilled by shaped tube electrochemical machining”, Int. J. Adv. Manuf .Technol., Vol. 39, No. 1-2, pp. 47-54, (2008). [17] Z. Uros, C. Frank and K. Edi, “Adaptive network based inference system for estimation of flank wear in end-milling”, Journal of materials processing technology, Vol. 209, No. 3, pp. 1504-1511, (2009). [18] U. Caydas, A. Hascalik and S. Ekici, “An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM”, Expert Systems with Applications, Vol. 36, No. 3, pp. 6135-6139, (2009). [19] J. Antony, “Design of Experiments for Engineers and Scientists”, Elsevier Science & Technology Books, ISBN: 0750647094, (2003). [20] R. Venkata Rao, “Advanced Modeling and Optimization of Manufacturing Processes”, Springer, (2011). [21] M. S. Chen, “Analysis and design of the multi-layer perceptron using polynomial basis functions”, Doctor of philosophy thesis, The University of Texas at Arlington, (1991). [22] C. C. Klimasauskas, “Neural Computing. Neural- Ware”, Pittsburgh, (1991). [23] A. Schmidt, “A modular neural network architecture with additional generalization abilities for high dimensional input vectors”, Master of Science thesis, Manchester Metropolitan University, (1996). [24] K. B. Khanchandani and M. A. Hussain, “Emotion recognition using multilayer perceptron and generalized feed forward neural network”, Journal of scientific and industrial research, Vol. 68, pp. 367-371, (2009). [25] X. Li, X. Guan, Y. Li and A. Hybrid “Radial Basis Function Neural Network for Dimensional Error Prediction in End Milling”, ISNN 2004, LNCS 3174, pp. 743-748, (2004). [26] N. Q. Dinh and N. V. Afzulpurkar, “Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln”, Simulation Modelling Practice and Theory, Vol. 15, No. 10, pp. 1239-1258, (2007). [27] J. S. Pan, S. M. Chen and N. T. Nguyen, “Intelligent Information and Database Systems, 4th Asian Conference”, ACIIDS Kaohsiung, Taiwan, March 19-21, 201 Proceedings, Part III, (2012).
آمار تعداد مشاهده مقاله: 2,582 تعداد دریافت فایل اصل مقاله: 3,285

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Investigating electrochemical drilling (ECD) using statistical and soft computing techniques