Browsing by Author "Ait Chikh, Mohamed Abdessamed"

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    An improved artificial neural network using weighted mean of vectors algorithm for precise GTAW weld quality prediction and parameter optimization
    (Springer Science and Business Media, 2026) Boucetta, Brahim; Boumediene, Faiza; Ait Chikh, Mohamed Abdessamed; Afia, Adel
    Accurate prediction of mechanical properties in gas tungsten arc welding (GTAW) remains challenging due to the complex, nonlinear relationships between process parameters and weld quality. This study introduces a novel framework that systematically evaluates seven state-of-the-art metaheuristic algorithms: spider wasp optimizer (SWO), weighted mean of vectors (INFO), gradient-based optimizer (GBO), artificial rabbits optimization (ARO), blood-sucking leech optimizer (BSLO), RUN beyond the metaphor (RUN), and successive history adaptive differential evolution (SHADE), for training artificial neural networks (ANNs) to predict ultimate tensile strength in GTAW of Inconel 825 alloy. The primary novelty lies in identifying the gradient-based optimizer as the most effective algorithm for this application, presenting superior generalization capability and establishing a new benchmark for welding parameter prediction. The optimized ANN-GBO model achieved significant performance improvements over conventional ANN approaches, with the coefficient of determination () increasing from 0.6844 to 0.8669 (26.7% improvement) and root mean square error (RMSE) decreasing from 51.89 MPa to 33.71 MPa (35.0% reduction). These substantial enhancements in prediction accuracy provide critical insights for optimizing high-performance nickel-based alloy welding processes
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    Analysis of natural convection heat transfer in a rectangular cavity with discrete heat flux: Implications for building thermal management using artificial neural networks
    (Taylor and Francis Ltd., 2024) Rachedi, Kamel; Ragueb, Haroun; Behnous, Dounya; Tahiri, Antar; Manser, Belkacem; Ait Chikh, Mohamed Abdessamed
    This study numerically investigates free convection within a rectangular air-filled cavity, simulating real-romm conditions. The top, bottom, and one sidewall are at constant temperatures, while the opposite sidewall has a constant discrete heat flux, akin to heater appliances. The impact of heating intensity, length, and position on temperature distribution is explored. Artificial Neural Networks (ANN) are utilized to correlate the average Nusselt number, providing a model for engineering applications in building thermal management. The dataset includes 2436 simulation runs with varying parameter: Rayleigh number (103 to 106), aspect ratio (0.5 to 2), heating surface length (0.1 to 1), and elevation (0.05 to 0.95). Results show increased Rayleigh numbers intensify the stream function and promote uniform temperature distribution. The elevation of the heating surface influences temperature distribution, with placement closer to the floor or ceiling optimizing heat transfer. ANN modeling predicts the average Nusselt number with high precision (±3%).
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    Computer numerical control machine tool wear monitoring through a data-driven approach
    (SAGE, 2024) Gougam, Fawzi; Afia, Adel; Ait Chikh, Mohamed Abdessamed; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, Djamel
    The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methods