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Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond

Author

Listed:
  • Pauline Ong

    (Universiti Tun Hussein Onn Malaysia (UTHM))

  • Chon Haow Chong

    (Universiti Tun Hussein Onn Malaysia (UTHM))

  • Mohammad Zulafif Rahim

    (Universiti Tun Hussein Onn Malaysia (UTHM))

  • Woon Kiow Lee

    (Universiti Tun Hussein Onn Malaysia (UTHM))

  • Chee Kiong Sia

    (Universiti Tun Hussein Onn Malaysia (UTHM))

  • Muhammad Ariff Haikal Ahmad

    (Universiti Tun Hussein Onn Malaysia (UTHM))

Abstract

Polycrystalline diamond (PCD) is increasingly becomes an important material used in the industry for cutting tools of difficult-to-machine materials due to its excellent characteristics such as hardness, toughness and wear resistance. However, its applications are restricted because of the PCD material is difficult to machine. Therefore, electrical discharge machining (EDM) is an ideal method suitable for PCD materials due to its non-contact process nature. The performance of EDM, however, is significantly influenced by its process parameters and type of electrode. In this study, soft computing technique was utilized to optimize the performance of the EDM in roughing condition for eroding PCD with copper tungsten or copper nickel electrode. Central composite design with five levels of three machining parameters viz. peak current, pulse interval and pulse duration has been used to design the experimental matrix. The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). It was found that copper tungsten electrode gave lower EWR, in comparison with the copper nickel electrode. The predictive model of radial basis function neural network (RBFNN) was developed to predict the MRR and EWR of the EDM process. The prominent predictive ability of RBFNN was confirmed as the prediction errors in terms of mean-squared error were found within the range of 6.47E−05 to 7.29E−06. Response surface plot was drawn to study the influences of machining parameters of EDM for shaping PCD with copper tungsten and copper nickel. Subsequently, moth search algorithm (MSA) was used to determine the optimal machining parameters, such that the MRR was maximized and EWR was minimized. Based on the obtained optimal parameters, confirmation test with the absolute error within the range of 1.41E−06 to 5.10E−05 validated the optimization capability of MSA.

Suggested Citation

  • Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1443-6
    DOI: 10.1007/s10845-018-1443-6
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    References listed on IDEAS

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    1. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    2. Kumar Abhishek & V. Rakesh Kumar & Saurav Datta & Siba Sankar Mahapatra, 2017. "Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1769-1785, December.
    3. 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.
    4. Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
    5. Hamed Pashazadeh & Yousof Gheisari & Mohsen Hamedi, 2016. "Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 549-559, June.
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    Cited by:

    1. Juan Li & Yuan-Hua Yang & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Moth Search: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 10(21), pages 1-19, November.

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