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    Impact Behavior Analysis of Luffa/Epoxy Composites Under Low-Velocity Loading
    (Springer Nature, 2024) Grabi, Massinissa; Chellil, Ahmed; Lecheb, Samir; Grabi, Hocine; Nour, Abdelkader
    Luffa cylindrical (LC) has an exceptionally multipartite architecture, a hierarchical and light structure, and a low density. Such a structure is potentially suitable to replace conventional porous-type composites for low-energy absorption and material reinforcement applications. This paper presents an experimental study of the impact behavior of four different luffa/epoxy composites, named (A), (B), (C), and (D) subjected to low-velocity impact (LVI) at energies ranging from barely visible impact damage (BVID) to perforation (5,15, and 20J). Acoustic emission (AE), scanning electron microscopy (SEM), and digital image correlation (DIC) were introduced to the indentation test to offer additional information on damage mechanisms and on strain and displacement fields since the LVI test has a short duration and real-time damage monitoring is not always achievable. The results showed that the values of the peak force of laminates (A), (B), and (D) are relatively lower compared to laminates (C). In the case of perforation impact energy (20J), the Coefficients of Restitution (CoR) of composites (A), (B), and (D) are equal to 0, which indicates that the nature of the impact is completely plastic, except for composite (C) had a value of 0.11, and a lower degree of damage at all impact energies. Composites (C) exhibit the highest impact resistance, followed by composites (A), while composites (D) display the highest energy absorption, followed by composites (B). Multivariable statistical analysis of the AE signals identified four classes of damage: matrix cracking, fiber-matrix debonding, delamination, and fiber breakage. The damage modes found by AE are well presented and proven by SEM analysis. The luffa fiber-reinforced composite has better impact properties than other natural fiber-reinforced composites.
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    Alkaline Treatment’s Effect on Mechanical Properties and Damage Assessment Through Acoustic Emission of Luffa Fiber Composite
    (Springer, 2022) Grabi, Massinissa; Chellil, Ahmed; Habibi, Mohamed; Laperriere, Luc
    Improving the mechanical properties and reduced damage of natural fiber-reinforced composites can contribute to their increased use in various fields. In this paper, an experimental study describes the effect of alkaline treatment of two different concentrations of 2 % and 5 % NaOH for one hour on the mechanical performance and damage of luffa fiber composites. Three different composites reinforced with treated and untreated luffa fibers were developed using the resin transfer molding (RTM) process. The specimens were coupled with acoustic emission during tensile tests, to monitor and evaluate damage mechanisms. The tensile test results showed that the alkaline treatment of 5 % improved tensile strength, which reached 81.08±1.48 MPa. However, the 2 % treatment improved Young’s modulus with 8.94±0.5 GPa. In comparison, T2 % and T5 % composites provided the best results for mechanical properties compared to NT composites. Four classes of damage mechanisms have been identified using the K-means clustering method, including matrix cracking, fiber pull-out, delamination, and fiber breakage. The cumulated energy and hits of the 5 % treated composite was lower than the untreated and 2 % treated, which means less damage to the T5 % specimen. Scanning electron microscopy (SEM) pictures of the tensile fractured surfaces of luffa fiber composites treated with 5 % NaOH, revealed good adhesion between the fibers and the matrix. The AE results are convincing, and they were confirmed by SEM pictures of the specimens’ fractured faces, which revealed the main causes of material failure, So, based on the AE results and mechanical properties, T5 % composite is preferable.
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    Characterization of low-velocity impact and post-impact damage of luffa mat composite using acoustic emission and digital image correlation
    (SAGE Publications, 2022) Grabi, Massinissa; Chellil, Ahmed; Habibi, Mohamed; Grabi, Hocine; Laperriere, Luc
    In this paper, low-velocity impact and compression after impact damage tolerance of composite reinforced with natural luffa mat were studied for the first time. The effect of impact energy and the influence of the damaged area on the residual mechanical properties under compression were investigated. Acoustic emission (AE), digital image correlation (DIC) and scanning electron microscopy (SEM) were used for the evolution of different damage modes and displacement fields. The findings of the experiments reveal that compression after impact tests of 1, 2, and 3J show a significant effect of the residual damage which decreases residual compressive strength by 12.61, 24.14, and 30.9%, respectively, compared to the unimpacted composite, but Young’s modulus was not significantly affected. Multivariable statistical analysis of the AE signals identified four classes of damage: matrix cracking, fiber-matrix debonding, delamination, and fiber failure. It also showed that the damage mode of unimpacted composites which presents the majority of the amplitude events of the AE signals is mainly due to fiber failure, by contrast, for impacted composites the damage mode is mainly due to fiber-matrix debonding. The AE results are convincing and they were confirmed by SEM images of the fractured faces of the specimens, which revealed the main causes of material failure during the compression after impact test. The DIC system monitored the effect of pre-existing damage under compressive loading and found that increasing impact energy increases the stress concentration around the impacted area and has a significant effect on residual crack development, much more in the loading direction
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    Tool wear condition monitoring based on wavelet transform and improved extreme learning machine
    (Sage journals, 2019) Laddada, Sofiane; Ouali Si-Chaib, Mouhamed; Benkedjouh, Tarak; Drai, Redouane
    In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.