Prediction and Optimization of Mold Steel Hardness in Wire Cut Process Based on Fuzzy Inference System

Document Type : Original Article

Authors

1 Faculty of Engineering, Ilam University, Ilam, Iran.

2 Faculty of Engineering, Ilam University, Ilam, Iran

3 Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran

Abstract

Pressing molds are widely used in the industry due to the high speed of parts production. Due to the very low clearance between the mandrel and the matrix and having complex shapes, the production of these molds is very expensive and costly.  The hardness of the mold and subsequently their fatigue life should be high. One of the processes that has the ability to create surfaces with high dimensional accuracy and complex shapes is the wirecut process. In this research, in order to improve the performance and increase the life of cutting dies, the hardness of Mo40 steel is optimized. For this purpose, based on the method of the response procedure, the design of the experiment has been designed for the three parameters of wire feed speed, wire tension and generator power. Next, the obtained data are clustered and then fuzzy rules with three inputs (wire speed, tension and power) and output (hardness) are extracted. The obtained rules were entered in the toolbox of fuzzy inference systems of MATLAB software. Based on the defined fuzzy inference system, it is possible to predict the hardness based on the parameters of wire speed, wire tension and generator power. In the next phase, based on this system and within the range of available variables, the optimal value of hardness and corresponding variables were extracted. In the final phase, these values ​​were experimentally tested and their agreement with the obtained value was observed.

Keywords

Main Subjects

References

  1. Singh, S. and Bhardwaj, A., “Review to EDM by using water and powder-mixed dielectric fluid,” Minerals and Materials Characterization and Engineering, vol. 10, no. 2, pp. 199, 2011.
  2. Majumder, H. and Maity, K., “Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM,” Measurement, vol. 118, pp. 1-13, 2018.
  3. Singh, J., Singh, R. and Kumar, R., “Review on effects of process parameters in wire cut EDM and wire electrode development,” International Journal of Innovative Research in Science, vol. 2, pp. 701-706, 2016.
  4. Ho, K.H., Newman, S.T., Rahimifard, S. and Allen, R.D., “State of the art in wire electrical discharge machining (WEDM),” International Journal of Machine Tools and Manufacture, vol. 44, pp. 12-13, 2004.
  5. Snoeys, R. and Van Dijck, F., “Plasma channel diameter growth affects stock removal in EDM,” 21, no.1, pp. 39-40, 1972.
  6. Pandit, S.M. and Rajurkar, K.P., “A stochastic approach to thermal modeling applied to electro-discharge machining,” Heat Transfer, vol. 105, pp. 555-562, 1983.
  7. Pandey, P.C. and Jilani, S.T., “Plasma channel growth and the resolidified layer in EDM,” Precision Engineering, vol. 8, pp. 104-110, 1986.
  8. Shankar, P., Jain, V.K. and Sundararajan, T., “Analysis of spark profiles during EDM process,” Machining Science and Technology, vol. 1, pp. 195-217, 1997.
  9. Guu, Y.H. and Hocheng, H., “Improvement of fatigue life of electrical discharge machined AISI D2 tool steel by TiN coating,” Materials Science and Engineering, vol. 318, pp. 155-162, 2001.
  10. Jeelani, S. and Collins, M.R., “Effect of electric discharge machining on the fatigue life of Inconel 718,” International Journal of Fatigue, vol. 10, pp. 121-125, 1988.‏
  11. Zeid, O.A., “On the effect of electrodischarge machining parameters on the fatigue life of AISI D6 tool steel,”Journal of Materials Processing Technology, vol. 68, pp. 27-32, 1997.
  12. Ramulu, M., Paul, G. and Patel, J., “EDM surface effects on the fatigue strength of a 15 vol% SiCp/Al metal matrix composite material,” Composite Structures, vol. 54, pp. 79-86, 2001.
  13. Khan, N., Wahid, M., Singh, S., Siddiquee, A. and Khan, Z., “A study on micro hardness in wire electrical discharge machining based on taguchi method,” International Journal of Mechanical and Production Engineering, vol. 1, pp.10-15, 2013.
  14. Lotfi Neyestanak, A., Daneshmand, S. and Adib Nazari, S., “The effect of operational cutting parameters in the wire electro discharge machining (WEDM) on micro hardness of alloy surface layer,” International Journal of Advanced design and Manufacturing Technology, vol. 2, no. 4, pp.51-58, 2010. (In Persian)
  15. Mahmoudinia, I., Soleimanimehr, H., maghsoudpour, A. and Etemadi Haghighi, S.H., “Fractal-based analysis of the influence of current intensity on surface hardness and surface roughness of Steel workpiece in Electrical Discharge process,” Iranian Journal of Manufacturing Engineering, vol. 7, no. 11, pp. 21-33, 2021. (In Persian)
  16. Kiyak, M., “Investigation of effects of cutting parameters on surface quality and hardness in the wire-EDM process,” International Journal of Advanced Manufacturing Technology, vol. 119, pp. 647-655, 2022.
  17. Kavitha, C., Malini, P.G., Kantumuchu, V.C., Kumar, N.M., Verma, A. and Boopathi, S., “An experimental study on the hardness and wear rate of carbonitride coated stainless steel,” Materials Today: Proceedings, vol. 1, 2022.
  18. Paturi, U.M.R., Cheruku, S., Pasunuri, V.P.K., Salike, S., Reddy, N.S. and Cheruku, S., “Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining,”Machine Learning with Applications, vol.  6, 2021.
  19. Rao, M., Vijayan, V., Anil, A., Rai, P.K .and Jain, N. R., “Effect of wire electrode discharge machining process parameters on the surface roughness, hardness, and microstructure of the high carbon steels,” Materials Today: Proceedings, vol.46, pp. 2625-2629, 2021.
  20. Das, P.P. and Chakraborty, S., “A grey correlation-based TOPSIS approach for optimization of surface roughness and micro hardness of Nitinol during WEDM operation,” Materials Today: Proceedings, vol. 28, pp. 568-573, 2020.
  21. Manjunatha, B.B., Ravindra, H.V. and Kuruvila, N., “Estimation of surface roughness and dimentional accuracy using process parameters in wire cut EDM by attificial neural network,” International of Symposium on Measurement and Quality Control, vol. 1, 2007.
  22. Boadh, R., Yadav, S.N., Tiwari, A., Rajoria, Y.K. and Singh, J. “Application of fuzzy inference system (FIS) for assessment and predication of compressive asset of concrete containing fly ash,” Materials Today: Proceedings, vol. 69, pp. 107-111, 2022.
  23. ‏Vechione, M. and Cheu, R.L., “Comparative evaluation of adaptive fuzzy inference system and adaptive neuro-fuzzy inference system for mandatory lane changing decisions on freeways,” Intelligent Transportation Systems, vol. 26, pp. 746-760, 2021.
  24. Wu, Y., Guo, H., Song, H. and Deng, R., “Fuzzy inference system application for oil-water flow patterns identification,” Energy, vol. 239, pp. 122359, 2022.
  25. Bo, L., Han, L., Xiang, C., Liu, H. and Ma, T., “A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles,” Energy, vol. 252, pp. 123976, 2022.
  26. Valizadeh, M., Braki, Z.A., Rashidi, R., Maghfourian, M. and Shenas, A.T., “Fuzzy inference system and adaptive neuro-fuzzy inference system approaches based on spectrophotometry method for the simultaneous determination of salmeterol and fluticasone in binary mixture of pharmaceutical formulation,” Optik, vol. 244, pp. 167599, 2021.
  27. Mamdani, E.H. and Assilian, S., “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” International Journal of Man-Machine Studies, 7, pp. 1-13, 1975.
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics