Optimization Multi Response on Electrical Discharge Machining Sinking Process Using Taguchi-Grey-Fuzzy Methods
Abstract
Electronic Discharge Machining (EDM) sinking applied widely in advance material manufacturing, every process parameter will count on this company. Their performance evaluated by some parameters such as surface roughness and tool wear ratio. Then they will be a dependent variable on this research. Independent variables on this research are electrode polarization, gap voltage, duty factor and pulse current. Every variable has three levels, except electrode polarization has two levels. This research conducting using Taguchi matrix orthogonal L18 (21×33) methods. The aim of this experiment is to evaluate optimization parameter process on EDM sinking, using Taguchi-Grey-Fuzzy methods. Characteristics response optimal applied are ‘smaller better’ for surface response roughness and tool wear ratio. This research using DAC tool steel as work-piece. DAC is most widely used as die for aluminum and zinc die-casting. The aim of this research is finding contribution of variable in EDM sinking parameter. Result of this research show contribution from variable process to reduce variance total observed response simultaneously, in order are electrode polarization on 49,53%, gap voltage on 23,52%, duty factor on 5,45% and pulse current on 9,92%. From validated optimization in confirmation experiment, to conclude combination variable process optimal response value is electrode polarization on positive, gap voltage at 50V, duty factor at 0.5 and pulse current at 12A.
Keywords
Full Text:
PDFReferences
A. K. Singh, R. Mahajan, A. Tiwari, D. Kumar, and R. K. Ghadai, ‘Effect of Dielectric on Electrical Discharge Machining: A Review’, IOP Conf. Ser. Mater. Sci. Eng., vol. 377, no. 1, 2018, doi: 10.1088/1757-899X/377/1/012184.
K. . Ho and S. . Newman, ‘State of the art electrical discharge machining (EDM)’, Int. J. Mach. Tools Manuf., vol. 43, no. 13, pp. 1287–1300, Oct. 2003, doi: 10.1016/S0890-6955(03)00162-7.
V. S. Jatti, ‘Multi-characteristics optimization in EDM of NiTi alloy, NiCu alloy and BeCu alloy using Taguchi’s approach and utility concept’, Alexandria Eng. J., vol. 57, no. 4, pp. 2807–2817, 2018, doi: 10.1016/j.aej.2017.11.004.
M. Zhou, J. Wu, X. Xu, X. Mu, and Y. Dou, ‘Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws’, Mech. Syst. Signal Process., vol. 101, pp. 480–497, 2018, doi: 10.1016/j.ymssp.2017.06.041.
T. Leppert, ‘A review on ecological and health impacts of electro discharge machining (EDM)’, AIP Conf. Proc., vol. 2017, no. October, 2018, doi: 10.1063/1.5056277.
D. N. Passarella, F. Varas, and E. B. Martín, ‘Heat transfer model for quenching by submerging’, J. Phys. Conf. Ser., vol. 296, no. 1, 2011, doi: 10.1088/1742-6596/296/1/012004.
S. Oh and H. Ki, ‘Deep learning model for predicting hardness distribution in laser heat treatment of AISI H13 tool steel’, Appl. Therm. Eng., vol. 153, pp. 583–595, May 2019, doi: 10.1016/J.APPLTHERMALENG.2019.01.050.
T. Vijaya Babu and J. S. Soni, ‘Investigation of process parameters optimization in die-sinking and wire cut EDM to improve process performance using taguchi technique’, Mater. Today Proc., vol. 5, no. 13, pp. 27088–27093, Jan. 2018, doi: 10.1016/J.MATPR.2018.09.014.
K. Wang, Q. Zhang, G. Zhu, Y. Huang, and J. Zhang, ‘Influence of tool size on machining characteristics of micro-EDM’, Procedia CIRP, vol. 68, no. April, pp. 604–609, 2018, doi: 10.1016/j.procir.2017.12.122.
H. Huang, M. Li, G. Chi, and Z. Wang, ‘Development of the adjusting and inspecting equipment for EDM’, no. Nceece, pp. 715–721, 2016, doi: 10.2991/nceece-15.2016.133.
H. P. Nguyen and V. D. Pham, ‘Single objective optimization of die- sinking electrical discharge machining with low frequency vibration assigned on workpiece by taguchi method’, J. King Saud Univ. - Eng. Sci., no. xxxx, pp. 1–6, 2019, doi: 10.1016/j.jksues.2019.11.001.
E. S. Gadelmawla, M. M. Koura, T. M. A. Maksoud, I. M. Elewa, and H. H. Soliman, ‘Roughness parameters’, J. Mater. Process. Technol., vol. 123, no. 1, pp. 133–145, 2002, doi: 10.1016/S0924-0136(02)00060-2.
X. Fu, L. Gao, Q. Zhang, and Q. Liu, ‘Surface Roughness Research of Piezoelectric Self-adaptive Micro-EDM’, Procedia CIRP, vol. 42, no. Isem Xviii, pp. 563–568, 2016, doi: 10.1016/j.procir.2016.02.252.
R. A. Anugraha, M. Y. Wiraditya, M. Iqbal, and N. M. Darmawan, ‘Application of Taguchi Method for Optimization of Parameter in Improving Soybean Cracking Process on Dry Process of tempeh Production’, IOP Conf. Ser. Mater. Sci. Eng., vol. 528, no. 1, 2019, doi: 10.1088/1757-899X/528/1/012070.
S. Winarni and S. W. Indratno, ‘Application of multi response optimization with grey relational analysis and fuzzy logic method’, J. Phys. Conf. Ser., vol. 948, no. 1, 2018, doi: 10.1088/1742-6596/948/1/012075.
A. K. Rouniyar and P. Shandilya, ‘Multi-Objective Optimization using Taguchi and Grey Relational Analysis on Machining of Ti-6Al-4V Alloy by Powder Mixed EDM Process’, Mater. Today Proc., vol. 5, no. 11, pp. 23779–23788, 2018, doi: 10.1016/j.matpr.2018.10.169.
H. B. Özerkan, ‘Effect of electrode polarity on fatigue life in EDM’, MATEC Web Conf., vol. 224, 2018, doi: 10.1051/matecconf/201822401107.
H. Hanizam, N. Mohamad, U. A. A. Azlan, and M. Qamaruddin, ‘Process Optimization of EDM Cutting Process on Tool Steel using Zinc Coated Electrode’, MATEC Web Conf., vol. 97, pp. 0–4, 2017, doi: 10.1051/matecconf/20179701073.
DOI
https://doi.org/10.21107/ijseit.v4i2.6705Metrics
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 International journal of science, engineering, and information technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.