Optimization and prediction of laser micro-grooving by artificial neural network

  • Authors

    • Sivarao . Advanced Manufacturing Centre, Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka
    • A. Z. Khalim Universiti Teknikal Malaysia Melaka Malaysia
    • Yusliza Yusoff School of Computing, Faculty of Engineering,81310 UTM Skudai, Johor Bahru, Johor
    • S Pujari Faculty of Mechanical Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, Vizag, India
    • D. Sivakumar Centre for Advanced Research on Energy, Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka
    • M. A. Amran Advanced Manufacturing Centre, Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka
    2019-06-30
    https://doi.org/10.14419/ijet.v7i4.24457
  • CO2 Laser Cutting, Laser Lathing, Micro-Grooving, Neural Network, Optimization.

  • Abstract

    Lasers are widely used in machining industry as a cutting tool ultra-flexible, produce high quality end product, quick set up, non-mechanical contact between the workpiece and the tool, and small size of the heat affected zone are some of the good features of laser cutting. However, laser cutting of sheet metal has limitation in machining of circular geometries by turning operation. There-fore, a novel approach has been devised to investigate in transforming a 2D flatbed CO2 laser cutting machine into 3D laser lathing capability as an alternative solution. In this paper, artificial neural networks (ANN) were employed to build a predictive model for depth quality of micro-grooving commercially pure (CP) titanium grade 2 by using a flatbed (CO2) laser cutting. The predicting model includes five input variables of the power, the gas pressure, the cutting speed, the focal distance and the depth of cut. It is found that from the ANN model developed, near optimum groove depth values are generated where the average prediction error is 6.48%.

     

     

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  • How to Cite

    ., S., Z. Khalim, A., Yusoff, Y., Pujari, S., Sivakumar, D., & A. Amran, M. (2019). Optimization and prediction of laser micro-grooving by artificial neural network. International Journal of Engineering & Technology, 7(4), 6481-6487. https://doi.org/10.14419/ijet.v7i4.24457

    Received date: 2018-12-20

    Accepted date: 2019-06-02

    Published date: 2019-06-30