Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel

In this study, experimental investigations and simulations were conducted to examine the influence of machining parameters during the turning process of C45 medium carbon steel, with the simultaneous application of the Johnson-Cook plasticity model and Johnson-Cook damage model. The validity of the computational results was confirmed by comparing them with experimental findings on chip morphology and temperature rise during cutting. The obtained results indicate that cutting depth significantly affects chip morphology during the turning process, while cutting speed has a minimal impact on the chip length at a cutting depth of 1 mm. Additionally, the temperature rise during cutting is primarily concentrated around the cutting tool nose, rather than near the chip or machined surface. Among the investigated cutting speeds, 0.46 m/s is identified as the most suitable for turning C45 steel at a cutting depth of 1 mm. Finally, for practical purposes, an artificial neural network model based on machine learning is developed to predict the average temperature near the turning insert nose.