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

Type
Articles dans des revues avec comité de lecture
Auteur
WANG, Zimo
301080 Texas A&M University [College Station]
DIXIT, Pawan
301080 Texas A&M University [College Station]
CHEGDANI, Faissal
211915 Mechanics surfaces and materials processing [MSMP]
301080 Texas A&M University [College Station]
TAKABI, Behrouz
301080 Texas A&M University [College Station]
TAI, Bruce L.
301080 Texas A&M University [College Station]
EL MANSORI, Mohamed
211915 Mechanics surfaces and materials processing [MSMP]
301080 Texas A&M University [College Station]
BUKKAPATNAM, Satish
301080 Texas A&M University [College Station]

URI
http://hdl.handle.net/10985/19635
DOI
10.1520/ssms20190042
Date
2020
Journal
Smart and Sustainable Manufacturing Systems

Résumé

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.

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  • Laboratoire Mechanics, Surfaces and Materials Processing (MSMP)

Documents liés

Visualiser des documents liés par titre, auteur, créateur et sujet.

  • Multiscale tribo-mechanical analysis of natural fiber composites for manufacturing applications 
    CHEGDANI, Faissal; WANG, Zimo; EL MANSORI, Mohamed; BUKKAPATNAM, Satish (ELSEVIER, 2018)
    This paper aims to investigate the tribo-mechanical behavior of natural fiber reinforced plastic (NFRP) composites with specific consideration of the multiscale complex structure of natural fibers. Understanding the ...
  • Thermal effect on the tribo-mechanical behavior of natural fiber composites at micro-scale 
    CHEGDANI, Faissal; EL MANSORI, Mohamed; BUKKAPATNAM, Satish T.S.; EL AMRI, Iskander (Elsevier BV, 2019)
    This paper aims to explore the thermal influence on the micro-tribo-mechanical behavior of natural fiber composites. Nanoindentation and scratch-test are used to characterize flax fibers reinforced polypropylene (PP) ...
  • Thermo-mechanical Effects in Mechanical Polishing of Natural Fiber Composites 
    CHEGDANI, Faissal; BUKKAPATNAM, Satish; EL MANSORI, Mohamed (ELSEVIER, 2018)
    Efficient machining and finishing of natural fiber reinforced plastic (NFRP) composites is essential for realizing the industrial application envisaged of these promising, environmentally friendly materials. While prior ...
  • Thermal Effects on Tribological Behavior in Machining Natural Fiber Composites 
    CHEGDANI, Faissal; TAKABI, Behrouz; TAI, Bruce; EL MANSORI, Mohamed; BUKKAPATNAM, Satish (ELSEVIER, 2018)
    Machining natural fibers reinforced plastic (NFRP) composites is nowadays a real challenge for academia and industries. These eco-friendly materials are emerging in automotive and aeronautical industries thanks to many ...
  • Micromechanical modeling of the machining behavior of natural fiber-reinforced polymer composites 
    CHEGDANI, Faissal; EL MANSORI, Mohamed; T. S. BUKKAPATNAM, Satish; REDDY, J. N. (Springer, 2019)
    This paper aims to develop a 2D finite element (FE) model at microscale for numerical simulation of the machining behavior of natural fiber-reinforced polymer (NFRP) composites. The main objective of this study is to ...

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