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- 저자 : Noureddine Barka (검색결과 2 건)
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Rachid Fakir,Noureddine Barka,Jean Brousseau 대한금속·재료학회 2018 METALS AND MATERIALS International Vol.24 No.5
This paper proposes a statistical approach to analyze the mechanical properties of a standard test specimen, of cylindricalgeometry and in steel 4340, with a diameter of 6 mm, heat-treated and quenched in three different fl uids. Samples were evaluatedin standard tensile test to access their characteristic quantities: hardness, modulus of elasticity, yield strength, tensilestrength and ultimate deformation. The proposed approach is gradually being built (a) by a presentation of the experimentaldevice, (b) a presentation of the experimental plan and the results of the mechanical tests, (c) anova analysis of varianceand a representation of the output responses using the RSM response surface method, and (d) an analysis of the results anddiscussion. The feasibility and effectiveness of the proposed approach leads to a precise and reliable model capable of predictingthe variation of mechanical properties, depending on the tempering temperature, the tempering time and the coolingcapacity of the quenching medium.
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Mahdi Hadhri,Abderazzak El Ouafi,Noureddine Barka 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.2
This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process.
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