https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Sun, 08 Mar 2026 10:14:28 GMT 2026-03-08T10:14:28Z http://hdl.handle.net/10985/19635 Bidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber–Reinforced Polymer Composite Machining Process WANG, Zimo; DIXIT, Pawan; TAKABI, Behrouz; TAI, Bruce L.; BUKKAPATNAM, Satish; EL MANSORI, Mohamed; CHEGDANI, Faissal 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. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/19635 2020-01-01T00:00:00Z WANG, Zimo DIXIT, Pawan TAKABI, Behrouz TAI, Bruce L. BUKKAPATNAM, Satish EL MANSORI, Mohamed CHEGDANI, Faissal 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. http://hdl.handle.net/10985/19621 Effect of flax fiber orientation on machining behavior and surface finish of natural fiber reinforced polymer composites TAKABI, Behrouz; TAI, Bruce L.; BUKKAPATNAM, Satish T.S.; EL MANSORI, Mohamed; CHEGDANI, Faissal Manufacturing processes of natural fiber reinforced polymer (NFRP) composites are becoming the interest of industrials and scientists because these eco-friendly materials are emerging in automotive and aerospace industries. In this context, machining processes of NFRP composites present significant issues related to the complex structure of natural fibers that need thorough tribological studies. This paper aims to explore the effect of natural fiber orientation on the machinability of NFRP composites using Merchant model in order to separate the shearing energy from the friction energy. Orthogonal cutting process is conducted on unidirectional flax fibers reinforced polypropylene composites by changing the fiber orientation from 0° to 90° with respect to the cutting direction. Iosipescu shear tests are also performed to determine the mechanical shear behavior in function of the fiber orientation. Results show the applicability of Merchant model on the machining analysis of NFRP composites by verifying the main model assumptions. The fiber orientation affects significantly the shearing and the friction energies that control the cutting behavior and the chip formation of the NFRP composite. The resulted machined surfaces are hence intimately related to the natural fiber orientation. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/19621 2020-01-01T00:00:00Z TAKABI, Behrouz TAI, Bruce L. BUKKAPATNAM, Satish T.S. EL MANSORI, Mohamed CHEGDANI, Faissal Manufacturing processes of natural fiber reinforced polymer (NFRP) composites are becoming the interest of industrials and scientists because these eco-friendly materials are emerging in automotive and aerospace industries. In this context, machining processes of NFRP composites present significant issues related to the complex structure of natural fibers that need thorough tribological studies. This paper aims to explore the effect of natural fiber orientation on the machinability of NFRP composites using Merchant model in order to separate the shearing energy from the friction energy. Orthogonal cutting process is conducted on unidirectional flax fibers reinforced polypropylene composites by changing the fiber orientation from 0° to 90° with respect to the cutting direction. Iosipescu shear tests are also performed to determine the mechanical shear behavior in function of the fiber orientation. Results show the applicability of Merchant model on the machining analysis of NFRP composites by verifying the main model assumptions. The fiber orientation affects significantly the shearing and the friction energies that control the cutting behavior and the chip formation of the NFRP composite. The resulted machined surfaces are hence intimately related to the natural fiber orientation.