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Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes

Author

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  • Anshuman Kumar Sahu

    (National Institute of Technology)

  • Siba Sankar Mahapatra

    (National Institute of Technology)

Abstract

In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The machining performance measures considered in this study are material removal rate, tool wear rate and surface integrity of the machined surface measured in terms of average surface roughness (Ra), white layer thickness, surface crack density and micro-hardness on white layer. Since the machining process is a complex one, potentiality of application of a predictive tool such as least square support vector machine has been explored to provide guidelines for the practitioners to predict various machining performance measures before actual machining. The predictive model is said to be robust one as root mean square error in the range of 0.11–0.34 is obtained for various performance measures. A hybrid optimization technique known as desirability based grey relational analysis in combination with firefly algorithm is adopted for simultaneously optimizing the performance measures. It is observed that peak current and tool type are the significant parameters influencing all the performance measures.

Suggested Citation

  • Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-020-01624-8
    DOI: 10.1007/s10845-020-01624-8
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    References listed on IDEAS

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    1. Changqing Liu & Yingguang Li & Guanyan Zhou & Weiming Shen, 2018. "A sensor fusion and support vector machine based approach for recognition of complex machining conditions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1739-1752, December.
    2. Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    3. Zhonglei Liu & Xuekun Li & Dingzhu Wu & Zhiqiang Qian & Pingfa Feng & Yiming Rong, 2019. "The development of a hybrid firefly algorithm for multi-pass grinding process optimization," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2457-2472, August.
    4. Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
    5. Guiqian Liu & Xiangdong Gao & Deyong You & Nanfeng Zhang, 2019. "Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 821-832, February.
    6. Chinmaya P. Mohanty & Siba Sankar Mahapatra & Manas Ranjan Singh, 2016. "A particle swarm approach for multi-objective optimization of electrical discharge machining process," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1171-1190, December.
    7. Kalipada Maity & Himanshu Mishra, 2018. "ANN modelling and Elitist teaching learning approach for multi-objective optimization of $$\upmu $$ μ -EDM," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1599-1616, October.
    8. Feng Zhang & Taotao Zhou, 2019. "Process parameter optimization for laser-magnetic welding based on a sample-sorted support vector regression," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2217-2230, June.
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