Browsing by Author "Mohamed Slamani"

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    Correlation assessment and modeling of intra‑axis errors of prismatic axes for CNC machine tools
    (université M'sila, 2023) Ahlem Mechta; Mohamed Slamani; Moussa Zaoui; René Mayer; Jean‑François Chatelain
    This paper presents an experimental study conducted to assess the correlation between the intra-axis errors of prismatic axes for CNC machine tools. The validity and reliability of parametric models for the modeling of intra-axis errors (IAEs) of CNC machine tools in the context of indirect calibration are also assessed in this work. Three CNC machine tools with various controllers and guidance technologies were tested using two different measuring instruments. Two predictive models, namely Bézier and B-spline curves, are described and compared for the first time in this work. Both models are experimentally evaluated for accuracy and predictive efficiency using four evaluation criteria and new data sets from the three tested CNC machine tools. Results show a strong correlation between the positioning errors and the pitch and yaw errors for all the tested machines. The results also show that both proposed models are appropriate for the modeling of intra-axis errors, with the B-spline curves coming slightly on top in terms of performance. Moreover, with the same number of control points (n = 5), the two models provide residuals that are lower than the repeatability of the machine for most intra-axis errors tested. This experimental study thus confirms that a Bézier model of degree four and a B-spline model of degree two, both with five control points, are sufficient to represent the intra-axis errors for the tested CNC machine tools
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    Enhanced investigations and modeling of surface roughness of epoxy/ Alfa fber biocomposites using optimized neural network architecture with genetic algorithms
    (2023) Madani Grine; Mohamed Slamani; Aissa Laouissi; Mustapha Arslane; Mansour Rokbi
    Currently, there is a notable attraction within the industry towards biocomposites, driven by the increasing fascination with natural fber-reinforced composites (NFRCs). These NFRCs ofer remarkable benefts, including cost-efectiveness, biodegradability, eco-friendliness, and favorable mechanical properties. As a result, the manufacturing processes of natural fber reinforced polymer (NFRP) composites have garnered attention from both industrial professionals and scientists. The emergence of these eco-friendly materials in the automotive and aerospace industries has sparked interest in understanding their production techniques. However, the machining processes of NFRP composites pose signifcant challenges due to the complex structure of natural fbers, necessitating thorough studies to address these issues efectively. This research paper presents a comprehensive investigation on surface roughness during the milling process of Alfa/epoxy biocomposites. A set of 100 experimental trials was conducted to test the surface roughness, and analysis of variance (ANOVA) was used to assess the impact of cutting parameters and chemical treatment on surface quality. To develop a predictive model for surface roughness, a hybrid approach called ANN-GA (artifcial neural networks-genetic algorithms) is proposed in this research. This approach combines ANN and GA to determine an optimal neural network archi tecture. The performance of the ANN-GA model is compared to the Levenberg–Marquardt backpropagation (LM) algorithm. ANOVA results show that the feed per revolution have a signifcant infuence on surface roughness, followed by the chemi cal treatment of fbers, while machining direction has a smaller efect. The ANN-GA model demonstrates good accuracy in surface roughness prediction compared to the LM algorithm