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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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    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.