Logo do repositório

Percorrer por autor "Zemmouri, Amina"

A mostrar 1 - 2 de 2
  • Effect of carburizing time treatment on microstructure and mechanical properties of low alloy gear steels
    Publication . Boumediri, Haithem; Touati, Sofiane; Debbah, Younes; Selami, Salim; Chitour, Mourad; Khelifa, Mansouri; Kahaleras, Mohamed said; Boumediri, Khaled; Zemmouri, Amina; Athmani, Moussa; Fernandes, Filipe
    Gas carburizing significantly enhances the surface properties of low-alloy gear steels, resulting in superior micro-hardness, layer thickness, carbon content, and overall mechanical properties. Unlike other thermochemical processes such as nitriding and carbonitriding, which have limitations in core properties and hardening depth, gas carburizing offers unmatched surface hardness, wear resistance, and mechanical strength. This makes it ideal for demanding applications in the automotive, aerospace, and manufacturing industries. In this research, samples were gas-carburized for 4, 6, or 8 h. The results showed significant improvements: micro-hardness increased from approximately 140 HV to over 819 HV, and the surface layer thickness grew by more than 41%, from 1166 μm to 1576 μm. Additionally, the carbon content in the surface layer increased by over 450%, reaching up to 0.94 wt%. Clear correlations were observed between the duration of heating and the mechanical properties. Longer heating times, particularly after 8 h, raised ultimate tensile strength from 427.29 MPa to 778.33 MPa, while simultaneously decreasing elongation from 26.07% to 2.88% and resilience from 180 J cm−2 to 6.66 J cm−2. This optimization not only enhances surface hardness and durability but also improves key mechanical properties such as tensile strength, stiffness, resilience, and overall mechanical performance.
    2024Artigo científico Acesso aberto Ver mais
  • Performance analysis of steel W18CR4V grinding using RSM, DNN-GA, KNN, LM, DT, SVM models, and optimization via desirability function and MOGWO
    Publication . Fernandes, Filipe; Touati, Sofiane; Boumediri, Haithem; Karmi, Yacine; Chitour, Mourad; Boumediri, Khaled; Zemmouri, Amina; Moussa, Athmani
    This study presents an innovative approach to optimizing the grinding process of W18CR4V steel, a high-performance material used in reamer manufacturing, using advanced machine learning models and multi-objective optimization techniques. The novel combination of Deep Neural Networks with Genetic Algorithm (DNN-GA), K-Nearest Neighbors (KNN), Levenberg-Marquardt (LM), Decision Trees (DT), and Support Vector Machines (SVM) was employed to predict key process outcomes, such as surface roughness (Ra), maximum roughness height (Rz), and production time. The results reveal significant improvements, with Ra values ranging from 0.231 μm to 1.250 μm (up to 81.5 % reduction) and Rz from 1.519 μm to 6.833 μm (up to 77.7 % reduction). The hybrid DNN-GA model achieved R2 > 0.99, reducing prediction errors by 23–45 % compared to traditional models. Optimization via the Desirability Function achieved Ra values around 0.341 μm and Rz around 2.3 μm, with production times ranging from 1181 to 1426 s. The innovative Multi-Objective Grey Wolf Optimization (MOGWO) provided Pareto-optimal solutions, minimizing Ra to 0.3 μm, Rz to 1.5 μm, and production times between 2000 and 3000 s, offering better balance between surface quality and machining efficiency. This work highlights the unique integration of machine learning models with optimization techniques to significantly enhance grinding performance and manufacturing efficiency in high-precision industries.
    2025-02-28Artigo de investigação Acesso aberto Ver mais