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

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.

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

“…Meanwhile, with the continuous update of AE instruments, which are equipped with multi-channel and broadband sensors and real-time full waveforms which contain AEs, big data are collected. Therefore, AE technology and deep learning are linked and have been adopted by many researchers [7,[35][36][37][38][39][40][41]. The conversion of time series data into two-dimensional image data using short fast Fourier transform, wavelet transform, and the classification of acoustic emission data [42][43][44][45][46] using two-dimensional convolutional neural networks (CNNs) is a common method that has been used by many researchers.…”

Characterization of the physical origins of acoustic emission (AE) from natural fiber reinforced polymers (NFRPs) machining processes

“…Bukkapatnam et al [19] and Chang et al [20] investigated the characteristics of AE waveforms sourced from the cutting zone and related them to the elastic strain rate and stress generated from/due to plastic deformation during machining. However, currently the physical sources of AE have not been fully studied for the machining of NFRP materials [12,21]. Due to the microstructure variations of the fiber reinforced composites, failure/materials removal mechanisms are highly heterogeneous within localized regions.…”

Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

“…The results from the experimental study suggested that BD-GRNNs can correctly predict (around 87% accuracy) the cutting conditions. 19 For AE monitoring of composites, to investigate the aging effect of GFRP exposed to seawater environment for different periods of time, Suresh Kumar et al examined the mass gain ratio and flexural strength of GFRP laminates according to the changes in AE signal parameters for various periods of time after the seawater treatment. The significant AE parameters were considered as input data, which were taken from 40-70% of failure loads for developing the radial basis function NN and generalized regression neural network (GRNN) models, which both were able to predict the ultimate failure strength.…”