Artificial Neural Network Forecasting

  • Authors

    • Abdul Talib Bon
    • Hew See Hui
    2018-12-03
    https://doi.org/10.14419/ijet.v7i4.38.27894
  • Artificial Neural Network. Forecasting, Defect

  • Abstract

    Zero defect as a goal for the manufacturing sector especially when the factory engage in global market which the market is required a highest grade quality product. A defect will occur when it is fail to meet the intended design. Hence, defect prediction methods play an important role to forecast the number of product defect. For this study, Artificial Neural Network (ANN) used to forecast the product defect in furniture manufacturing in in order to develop a well suit ANN model for the product defect prediction and obtain an accurate prediction defect number for decision making. Colour defect as one of the product defect category. Therefore, data of colour defect was collected within eight (8) working hours for fourteen (14) days and the analysis process carried out by MATLAB R2015a application using the neural network toolbox. The neural network framework for the colour defect prediction was developed with the minimum error. The company is able to conduct prediction process with the framework and make a better decision based on the result in order to reach their goal.

     

     

     

  • References

    1. [1] Afrand, M., Toghraie, D., & Sina, N. (2016). Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: Development of a new correlation and modeled by artificial neural network. International Communications in Heat and Mass Transfer. http://doi.org/10.1016/j.icheatmasstransfer.2016.04.023

      [2] Ahmadloo, E., & Azizi, S. (2016). Prediction of thermal conductivity of various nanofluids using artificial neural network. International Communications in Heat and Mass Transfer, 74, 69–75. http://doi.org/10.1016/j.icheatmasstransfer.2016.03.008

      [3] Ariana, M. A., Vaferi, B., & Karimi, G. (2015). Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks. Powder Technology, 278, 1–10. http://doi.org/10.1016/j.powtec.2015.03.005

      [4] Azeem, N., & Usmani, S. (2011). Defect Prediction Leads to High Quality Product, 2011(November), 639–645. http://doi.org/10.4236/jsea.2011.411075

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  • How to Cite

    Talib Bon, A., & See Hui, H. (2018). Artificial Neural Network Forecasting. International Journal of Engineering & Technology, 7(4.38), 1436-1439. https://doi.org/10.14419/ijet.v7i4.38.27894

    Received date: 2019-02-24

    Accepted date: 2019-02-24

    Published date: 2018-12-03