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چکیده
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Hardened AISI 4340 steel is extensively utilized in engineering industries. Owing to the fact that thermo-mechanical loads are applied to surface layers during the hard machining process, severe changes occur at the surface of the machined workpiece. These changes can affect some mechanical properties of the material, including microhardness, and residual stresses generation in material. Therefore, it is significantly influenced by process conditions and there is not any study in literature to extensively evaluate and optimize different indications of microhardness alteration after hard machining of AISI 4340 Steel. At this study, the influence of machining parameters was firstly studied on the microhardness distribution at machining of hardened AISI 4340 steel by experimental tests. Then, the optimal surface microhardness and depth of the affected layer were assessed using the intelligent techniques. In this regards, the optimal process conditions were simultaneously determined using the combination of the Artificial Neural Networks (ANNs) and Non-dominated Sorting Genetic Algorithm (NSGA-II). The results indicated that, both depth of the affected layer and surface microhardness were obtained in optimal state when the cutting speed changes from 720 to 600 (Rpm) and feed rate and depth of cut are 0.05 (mm/rev) and 0.4 (mm), respectively.
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