Journal Published Online: 24 March 2020
Volume 4, Issue 2

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

CODEN: SSMSCY

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, USA
Dixit, Pawan
Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA Capital One Financial Corp, Richmond, VA, USA
Chegdani, Faissal
Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA Arts et Metiers Institute of Technology, MSMP, HESAM Université, Châlons-en-Champagne, France
Takabi, Behrouz
Texas A&M University, Department of Mechanical Engineering, College Station, TX, USA
Tai, Bruce L.
Texas A&M University, Department of Mechanical Engineering, College Station, TX, USA
El Mansori, Mohamed
Arts et Metiers Institute of Technology, MSMP, HESAM Université, Châlons-en-Champagne, France Texas Engineering Experiment Station, Institute for Manufacturing Systems, College Station, TX, USA
Bukkapatnam, Satish
Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA Texas Engineering Experiment Station, Institute for Manufacturing Systems, College Station, TX, USA
Pages: 20
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Stock #: SSMS20190042
ISSN: 2520-6478
DOI: 10.1520/SSMS20190042