Akademik Veri Yönetim Sistemi
Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, cilt.236, sa.3, ss.1096-1108, 2022 (SCI-Expanded)
In machining activities, sound emission is one of the key factors toward the operator's health and safety. Sound generation during cutting is the outcome of the interaction between tool and work. The intensity of sound greatly influences the cutting power consumption and surface finish obtained during machining. Therefore, the current work emphasized the analysis of sound emission, power consumption, and surface roughness in hard turning of AISI 4340 steel using a CBN tool which was rarely found in the literature. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to formulate the model for each response. The results indicated that the maximum value of input parameters exhibited the highest level of sound due to the creation of vibration in the machine and tool. Higher sound level indicates the generation of lower power consumption but at the same instant surface roughness was leading with increment in sound level. The feed rate exhibited the utmost noteworthy consequence on surface quality with 87.71% contribution. The cutting power can be decreased by choosing the high level of cutting parameters. The RSM and ANN have a good correlation with experimental data, but the accuracy of the ANN is better than the RSM.