https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Sun, 17 May 2026 09:46:46 GMT 2026-05-17T09:46:46Z http://hdl.handle.net/10985/17488 Thermal Effects on Tribological Behavior in Machining Natural Fiber Composites TAKABI, Behrouz; TAI, Bruce; BUKKAPATNAM, Satish; EL MANSORI, Mohamed; CHEGDANI, Faissal 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 benefits for sustainable development. It is then necessary to anticipate their machining processes for integrating them into the NFRP industrial production chains. This paper investigates the thermal effect on the machinability of unidirectional flax fibers reinforced polypropylene composites (UDF/PP) regarding to the cutting contact geometry. For this aim, orthogonal cutting process has been performed on UDF/PP composites at room and low temperature of composite samples. Cutting contact geometry has been explored by changing the tool rake angle. Results show that reducing the cutting temperature affects the chip morphology and improves the cutting behavior of flax fibers which ameliorates the machinability of UDF/PP composites. This machinability is also improved by cutting with a smaller positive rake angle that increases the cutting contact stiffness with flax fibers. This study allows determining a new relevant indicator parameter of NFRP machinability based on the cutting friction. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/17488 2018-01-01T00:00:00Z TAKABI, Behrouz TAI, Bruce BUKKAPATNAM, Satish EL MANSORI, Mohamed CHEGDANI, Faissal 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 benefits for sustainable development. It is then necessary to anticipate their machining processes for integrating them into the NFRP industrial production chains. This paper investigates the thermal effect on the machinability of unidirectional flax fibers reinforced polypropylene composites (UDF/PP) regarding to the cutting contact geometry. For this aim, orthogonal cutting process has been performed on UDF/PP composites at room and low temperature of composite samples. Cutting contact geometry has been explored by changing the tool rake angle. Results show that reducing the cutting temperature affects the chip morphology and improves the cutting behavior of flax fibers which ameliorates the machinability of UDF/PP composites. This machinability is also improved by cutting with a smaller positive rake angle that increases the cutting contact stiffness with flax fibers. This study allows determining a new relevant indicator parameter of NFRP machinability based on the cutting friction. 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. http://hdl.handle.net/10985/23602 Acoustic Emission Characterization of Natural Fiber Reinforced Plastic Composite Machining Using a Random Forest Machine Learning Model WANG, Zimo; CHEGDANI, Faissal; YALAMARTI, Neehar; TAKABI, Behrouz; TAI, Bruce; EL MANSORI, Mohamed; BUKKAPATNAM, Satish Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique structural properties. These materials are machined to create components that meet the dimensional and surface finish tolerance specifications for various industrial applications. The heterogeneous structure of these materials—resulting from different fiber orientations and their complex multiscale structure—introduces a distinct set of material removal mechanisms that inherently vary over time. This structure has an adverse effect on the surface integrity of machined NFRPs. Therefore, a real-time monitoring approach is desirable for timely intervention for quality assurance. Acoustic emission (AE) sensors that capture the elastic waves generated from the plastic deformation and fracture mechanisms have potential to characterize these abrupt variations in the material removal mechanisms. However, the relationship connecting AE waveform patterns with these NFRP material removal mechanisms is not currently understood. This paper reports an experimental investigation into how the time–frequency patterns of AE signals connote the various cutting mechanisms under different cutting speeds and fiber orientations. Extensive orthogonal cutting experiments on unidirectional flax fiber NFRP samples with various fiber orientations were conducted. The experimental setup was instrumented with a multisensor data acquisition system for synchronous collection of AE and vibration signals during NFRP cutting. A random forest machine learning approach was employed to quantitatively relate the AE energy over specific frequency bands to machining conditions and hence the process microdynamics, specifically, the phenomena of fiber fracture and debonding that are peculiar to NFRP machining. Results from this experimental study suggest that the AE energy over these frequency bands can correctly predict the cutting conditions to ∼95% accuracies, as well as the underlying material removal regimes. Fri, 31 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/23602 2020-01-31T00:00:00Z WANG, Zimo CHEGDANI, Faissal YALAMARTI, Neehar TAKABI, Behrouz TAI, Bruce EL MANSORI, Mohamed BUKKAPATNAM, Satish Natural fiber reinforced plastic (NFRP) composites are eliciting an increased interest across industrial sectors, as they combine a high degree of biodegradability and recyclability with unique structural properties. These materials are machined to create components that meet the dimensional and surface finish tolerance specifications for various industrial applications. The heterogeneous structure of these materials—resulting from different fiber orientations and their complex multiscale structure—introduces a distinct set of material removal mechanisms that inherently vary over time. This structure has an adverse effect on the surface integrity of machined NFRPs. Therefore, a real-time monitoring approach is desirable for timely intervention for quality assurance. Acoustic emission (AE) sensors that capture the elastic waves generated from the plastic deformation and fracture mechanisms have potential to characterize these abrupt variations in the material removal mechanisms. However, the relationship connecting AE waveform patterns with these NFRP material removal mechanisms is not currently understood. This paper reports an experimental investigation into how the time–frequency patterns of AE signals connote the various cutting mechanisms under different cutting speeds and fiber orientations. Extensive orthogonal cutting experiments on unidirectional flax fiber NFRP samples with various fiber orientations were conducted. The experimental setup was instrumented with a multisensor data acquisition system for synchronous collection of AE and vibration signals during NFRP cutting. A random forest machine learning approach was employed to quantitatively relate the AE energy over specific frequency bands to machining conditions and hence the process microdynamics, specifically, the phenomena of fiber fracture and debonding that are peculiar to NFRP machining. Results from this experimental study suggest that the AE energy over these frequency bands can correctly predict the cutting conditions to ∼95% accuracies, as well as the underlying material removal regimes.