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    Bidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber–Reinforced Polymer Composite Machining Process

    (Received 10 September 2019; accepted 18 February 2020)

    Published Online: 24 March 2020

    CODEN: SSMSCY

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    Abstract

    Natural fiber–reinforced polymer (NFRP) composites are increasingly considered in the industry for creating environmentally benign product alternatives. The complex structure of the fibers and their random distribution within the matrix basis impede the machinability of NFRP composites as well as the resulting product quality. This article investigates a smart process monitoring approach that employs acoustic emission (AE)—elastic waves sourced from various plastic deformation and fracture mechanisms—to characterize the variations in the NFRP machining process. The state-of-the-art analytic tools are incapable of handling the transient dynamic patterns with long-term correlations and bursts in AE and how process conditions and the underlying material removal mechanisms affect these patterns. To address this gap, we investigated two types of the bidirectional gated recurrent deep learning neural network (BD-GRNN) models, viz., bidirectional long short-term memory and bidirectional gated recurrent unit to predict the process conditions based on dynamic AE patterns. The models are tested on the AE signals gathered from orthogonal cutting experiments on NFRP samples performed at six different cutting speeds and three fiber orientations. The results from the experimental study suggest that BD-GRNNs can correctly predict (around 87 % accuracy) the cutting conditions based on the extracted temporal-spectral features of AE signals.

    Author Information:

    Wang, Zimo
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX

    Dixit, Pawan
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX

    Capital One Financial Corp, Richmond, VA

    Chegdani, Faissal
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX

    Arts et Metiers Institute of Technology, MSMP, HESAM Université, Châlons-en-Champagne,

    Takabi, Behrouz
    Texas A&M University, Department of Mechanical Engineering, College Station, TX

    Tai, Bruce L.
    Texas A&M University, Department of Mechanical Engineering, College Station, TX

    El Mansori, Mohamed
    Arts et Metiers Institute of Technology, MSMP, HESAM Université, Châlons-en-Champagne,

    Texas Engineering Experiment Station, Institute for Manufacturing Systems, College Station, TX

    Bukkapatnam, Satish
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX

    Texas Engineering Experiment Station, Institute for Manufacturing Systems, College Station, TX


    Stock #: SSMS20190042

    ISSN: 2520-6478

    DOI: 10.1520/SSMS20190042

    Author Zimo Wang, Pawan Dixit, Faissal Chegdani, Behrouz Takabi, Bruce L. Tai, Mohamed El Mansori, Satish Bukkapatnam
    Title Bidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber–Reinforced Polymer Composite Machining Process
    Symposium , 0000-00-00
    Committee E60