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Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model

Author

Listed:
  • Sudipto Chaki

    (MCKV Institute of Engineering)

  • Ravi N. Bathe

    (International Advance Research Centre for Power Metallurgy and New Materials (ARCI))

  • Sujit Ghosal

    (Jadavpur University)

  • G. Padmanabham

    (International Advance Research Centre for Power Metallurgy and New Materials (ARCI))

Abstract

The paper presents an integrated model of artificial neural networks (ANNs) and non-dominated sorting genetic algorithm (NSGAII) for prediction and optimization of quality characteristics during pulsed Nd:YAG laser cutting of aluminium alloy. A full factorial experiment has been conducted where cutting speed, pulse energy and pulse width are considered as controllable input parameters with surface roughness and material removal rate as output to generate the dataset for the model. In ANN–NSGAII model, back propagation ANN trained with Bayesian regularization algorithm is used for prediction and computation of fitness value during NSGAII optimization. NSGAII generates complete set of optimal solution with pareto-optimal front for outputs. Prediction accuracy of ANN module is indicated by around 1.5 % low mean absolute % error. Experimental validation of optimized output results less than 1 % error only. Characterization of the process parameters in pareto-optimal region has been explained in detail. Significance of controllable parameters of laser on outputs is also discussed.

Suggested Citation

  • Sudipto Chaki & Ravi N. Bathe & Sujit Ghosal & G. Padmanabham, 2018. "Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 175-190, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1100-2
    DOI: 10.1007/s10845-015-1100-2
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    Cited by:

    1. Zhen Zhang & Zenan Yang & Chenchong Wang & Wei Xu, 2024. "Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 449-465, January.
    2. Ammar H. Elsheikh & Taher A. Shehabeldeen & Jianxin Zhou & Ezzat Showaib & Mohamed Abd Elaziz, 2021. "Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1377-1388, June.
    3. Dongxiang Hou & Xiaodong Wang & Qing Song & Xuesong Mei & Haicheng Wang, 2024. "A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 331-341, January.

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