Exploring Aluminum Alloy Metal Matrix Composites in EDM Using Coupled Factor-level-present Worth Analysis and Fuzzy Analytic Hierarchy Process

Authors

  • K.O. Okponyia Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria
  • S.A. Oke University of Lagos, Lagos, Nigeria

DOI:

https://doi.org/10.24002/ijieem.v2i1.3781

Keywords:

Fuzzy set, machining planning, present worth method, analytical hierarchy process

Abstract

This paper targets two improvement aspects of the electrical discharge machining (EDM) process. First, it formulates the EDM problem as an economic issue incorporating the present worth analysis into the factor-level framework and solves it with the performance analysis flow diagram. Second, it conceptualizes the EDM process as imprecision and uncertainty and solves it with the fuzzy analytical hierarchy process (FAHP) approach. At present, these methods are not accessible to the EDM process engineer to machine the work material, AA6061/10%Al2O3AMMCs. In this study, the EDM process application using these methods on AA6061/10%Al2O3AMMCs is considered. This paper investigates the use of fuzzy AHP multi-criteria and the present worth method to produce a structured approach to model multi-response problem of EDM process parametric optimization concerning composite named AA6061/10%Al2O3AMMCs to obtain a robust design and the best parametric selection. The selected performance measures for the inputs to attain the performance flow analysis using the present worth method are MRR = 17.932 to 31.753 mg/min, TWR = 0.171 to 0.289 mg/min, SR = 8.228 to 12.680 mm and OV = 0.292 to 0.406 mm. The performance flow analysis reveals a present with a value of 1.604 based on the equivalence analysis of the flow diagram's positive and negative sides. The FAHP results showed the enhanced values of 0.1051, 0.2290, and 0.6658 for peak current, pulse on time, and duty factor, respectively. The approach is novel and has not been implemented elsewhere to solve the problem for the combination of materials studied.

Author Biographies

K.O. Okponyia, Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria

He is a student

S.A. Oke, University of Lagos, Lagos, Nigeria

He lectures at the University of Lagos

References

Afolayan A.H., Ojokoh B.A., Adetunmbi A.O. 2020. Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection, Scientific African, 9, 1–22.

Asilturk, I., Cunkas, M, 2010. Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method, Expert Systems with Applications, 38, 5826-5832.

Boussabaine A.H. and Elhag T., 1999, Applying fuzzy techniques to cash flow analysis, Construction Management and Economics, 17(6), 745-755.

Chen, Y., Mahdivian, S.M., 2000. Analysis of electro-discharge machining process and its comparision with experiments, Journal of Materials Processing Technology, 104, 150-157.

Chiang, K., 2008. Modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al2O3 + TiC mixed ceramic, International Journal of Advanced Manufacturing Technology, 37, 523–533.

Chiu C-Y and Park C.S, 1994. Fuzzy cash flow analysis using present worth criterion. The Engineering Economist, 39(2), 113-138.

Chockalingam K., Jawahar N., Muralidharan N., Jeyaraj K.L. 2019. Material subtraction study of AISI T-15-HSS by wire cut electrical discharge machining (CNC-wire cut EDM) based on Taguchi grey relational analysis, International Journal of Machining and Machinability of Materials, 21(3), 139-168.

Das M.K., Kumar K., Barman T.K., Sahoo P. 2014, Application of artificial bee colony algorithm for optimization of MRR and surface roughness in EDM of EN31 tool steel, Procedia Materials Science, 6, 741-751.

Das, M.K., Kumar, K., Barman, T.K., Sahoo, P., 2013. Optimization of surface roughness and MRR in EDM using WPCA. Procedia Engineering, 64, 446-455.

Dewangan S., Kumar S.D., Jha S.K., Biswas C.K. 2020, Optimization of Micro-EDM drilling parameters of Ti–6Al–4V alloy, Materials Today: Proceedings, 3, 1–5.

Doss D.A., Jones D.W., Sumrall W, Henley R., McErneath D., Lackey H., Gokaraju B., 2015, a net present worth analysis of considered academic programs at a private regional higher educational institution, Journal of Interdisciplinary Studies in Education, 4(1), 55-77.

Fazlollahtabar H., Gholizadeh H. 2020, Fuzzy possibility regression integrated with fuzzy adaptive neural network for predicting and optimizing electrical discharge machining parameters, Computers & Industrial Engineering, 140, 1–9.

Gupta V., Singh B. and Mishra R.K., 2020, Machining of titanium and titanium alloys by electric discharge machining process: a review, International Journal of Machining and Machinability of Materials, 22(2), 99–121.

Ilani, M.A., Khoshnevisan, M. 2020. Powder mixed-electrical discharge machining (EDM) with the electrode is made by fused deposition modeling (FDM) at Ti-6Al-4V machining procedure. Multiscale and Multidisciplinary Modeling, Experiments and Design, 3, 173–186.

Jaharah, A., Liang, C., Wahid, A., S.Z., Rahman, M., Hassan, C.C., 2008. Performance of copper electrode in electrical discharge machining (EDM) of AISI H13 harden steel. International Journal of Mechanical and Materials Engineering, 3, 25–29.

Kuppan, P., Rajadurai, A., Narayanan, S., 2007. Influence of EDM process parameters in deep hole drilling of Inconel 718, International Journal of Advanced Manufacturing Technology, 38, 74–84.

Kanagarajan, D., Karthikeyan, R., Palanikumar, K., Sivaraj, P., 2008. Influence of process parameters on electric discharge machining of WC/30%Co composites, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222, 807–815.

Kandpal B.C., Kumar J. and Singh H. 2017. Optimization and characterization of EDM of AA 6061/10%Al2O3 AMMC using Taguchi’s approach and utility concept, Production and Manufacturing Research, 5(1), 351–370.

Kandpal B.C., Kumar J., Singh H., 2018. Optimization of electrical discharge machining AA6061/10%Al2O3 composite using Taguchi optimization technique, Materials Today: Proceedings, 5(9), 18946–18955.

Karthikeyan G., Ramkumar J., Shalabh, Aravindan S. 2012. Performance analysis of µED-milling process using various statistical techniques, International Journal of Machining and Machinability of Materials, 11(2), 183-203.

Lajis, M.A., Mohd Radzi H.C.D., Nurul Amin, A.K.M., 2009. The Implementation of Taguchi method on EDM process of tungsten carbide, European Journal of Scientific Research, 26, 609-617.

Li Z., Chow D.H.C., Ding D., Ying J., Hu Y., Chen H., Zhao W., 2020. The development and realisation of a multi-faceted system for green building planning: A case in Ningbo using the fuzzy analytical hierarchy process, Energy and Buildings, 226, 1–11.

Lin, Y.C., Yan, B.H., Huang, F.Y., 2001. Surface improvement using a combination of electrical discharge machining with ball burnish machining based on the Taguchi method, International Journal of Advanced Manufacturing Technology, 18, 673-682.

Mahdavinejad, R.A, 2008. Optimisation of electro discharge machining parameters, Journal of Achievement in Materials and Manufacturing Engineering, 27, 163-166.

Martínez-Alvarado R., Granda-Gutiérrez E.E., Hernández-Rodríguez A., Praga-Alejo R.J. 2020. Pulse classification for an electrochemical discharge machining process based on fuzzy logic approach. International Journal of Precision Engineering and Manufacturing, 21, 1–12.

Maravas A and Pantouvakis J-P., 2018, A new approach to studying net present value and the internal rate of return of engineering project under uncertainty of three-dimensional graphs, Advanced Management in Civil Engineering, 2018, 1–9.

Maurya R., Porwal R.K., Singh R. 2019, Concerning drifts to optimization techniques of wire-EDM process for titanium based super alloys: A review, Materials Today: Proceedings, 18(7), 4509-4514.

Onyegiri I.E. and Oke S.A. 2016. An analytic hierarchical approach to building airline safety management systems, Erzincan University Journal of Science and Technology, 9(3), 147-163.

Payal, H.S., Rajesh, C., Sarabjeet, S., 2008. Analysis of electro discharge machined surfaces of EN-31 tool steel, Journal of Scientific and Industrial Research, 67, 1072-1077.

Phate M., Toney S. & Phate V., 2020, Modelling and investigating the impact of EDM parameters on surface roughness in EDM of Al/Cu/Ni Alloy, Australian Journal of Mechanical Engineering, 1–14.

Pradhan,M.K., Biswas, C.K., 2008. Neuro-fuzzy model on material removal rate in electrical discharge machining in AISI D2 steel, Proceedings of the 2nd International and 23rd All India Manufacturing Technology, Design and Research Conference, 1, 469–474.

Pradhan, M.K., Das, R., Biswas, C.K., 2009. Comparisons of neural network models on surface roughness in electrical discharge machining, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223, 801-808.

Puertas, I., Luis, C.J., Alvarez, L., 2004. Analysis of the influence of EDM parameters on surface quality, MRR and EW of WC-Co, Journal of Materials Processing Technology, 153(1), 1026–1032.

Raji, A.O. and Oke, S.A., 2020, Optimization of EDM for AA6061/10%Al2O3 AMMC using Taguchi schemes and analytical hierarchy process for weight determination, Kufa Journal of Engineering, 11(3), 42-61.

Rao, G.K.M., Janardhana, G.R., Rao, D.H., Rao, M.S., 2008. Development of hybrid model and optimization of metal removal rate in electric discharge machining using artificial neural networks and genetic algorithm, ARPN Journal of Engineering and Applied Sciences, 209(3), 19-30.

Rao, R.V., Pawar, P.J., 2009. Modeling and optimization of process parameters of wire electrical discharge machining, Proceedings of the Institution of Mechanical Engineers, Journal of Engineering Manufacture, 223(11), 1431-1440.

Sahu S.N., Murmu S.K., Nayak N.C. 2019, Multi-objective optimization of EDM process with performance appraisal of GA based algorithms in neural network environment, Materials Today: Proceedings, 18(7), 3982-3997.

Schumacher B.M., Kramiptz R. and Kruth J.-P., 2013, Historical phase of ED development driven by the dual influence of "market pull" and "science push", Procedia CIRP, 6, 5-12.

Saaty T.L., 2008. Decision making with the analytic hierarchy process, International Journal of Services Sciences, 1(1), 83–98.

Sanghani C.R., Acharya G.D., Kothari K.D. 2020. A simulation-based approach for modeling of fraction of energy transfer to workpiece in electrical discharge machining, International Journal of Manufacturing Research, 15(3), 285-296.

Sarma D.K. and Singh M.A. 2020. Machining of thin sections using multi-pass wire electrical discharge machining process, International Journal of Machining and Machinability of Materials, 22(1), 62–78.

Song Q., Jiang P., Zheng S. 2021. The application of cloud model combined with non-linear fuzzy analytic hierarchy process for the safety assessment of chemical plant production process, Process Safety and Environmental Protection, 145, 12-22.

Upadhyay L., Aggarwal M.L., Pandey P.M., 2019. Experimental investigations into rotary magnetic field and tool assisted electric discharge machining using magneto rheological fluid as dielectric, International Journal of Mechatronics and Manufacturing Systems, 12(1), 1-19.

Valaki J., Rathod P., Sidpara A., 2018, Sustainability Issues in Electric Discharge Machining, In Innovations in Manufacturing for Sustainability, 53–75.

Valaki, J.B., Rathod, P.P. 2016, Assessment of operational feasibility of waste vegetable oil based bio-dielectric fluid for sustainable electric discharge machining (EDM). International Journal of Advanced Manufacturing Technology, 87, 1509–1518.

Vignesh M. and Ramanujam R. 2018. Response optimisation in wire electrical discharge machining of AISI H11 tool steel using Taguchi - GRA approach, International Journal of Machining and Machinability of Materials, 20(5), 474-495.

Yazdi M., Korhan O. & Daneshvar S. 2020. Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in the process industry, International Journal of Occupational Safety and Ergonomics, 26(2), 319-335.

Zhang E., Zhuo J., Hou L., Fu C., Guo T., 2021. Comprehensive annoyance modeling of forklift sound quality based on rank score comparison and multi-fuzzy analytic hierarchy process, Applied Acoustics, 173, 1–8.

Zizlavsky O., 2014, Net present value approach: Method for economic assessment of innovation projects, Procedia-Social and Behavioral Sciences, 156, 506-512.

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Published

2020-06-15

How to Cite

Okponyia, K., & Oke, S. (2020). Exploring Aluminum Alloy Metal Matrix Composites in EDM Using Coupled Factor-level-present Worth Analysis and Fuzzy Analytic Hierarchy Process. International Journal of Industrial Engineering and Engineering Management, 2(1), 25–44. https://doi.org/10.24002/ijieem.v2i1.3781

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