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The ultrasonic vibration-assisted turning (UAT) is regarded as an advanced machining technique exhibiting distinct advantages over the conventional turning (CT). Superior surface finish of workpieces machined by UAT is one of the advantages. A robust model predicting the surface roughness of the workpieces after UAT is highly desirable when planning the appropriate manufacturing processes. In the present study, the influence of parameters including vibration amplitude, depth of cut, feed rate, and cutting speed on the surface roughness of a workpiece being machined by UAT has been investigated. For this purpose, extensive full factorial UAT experiments were carried out. Similar CT experiments were also performed for comparison with the results of UAT. A mathematical model was then proposed for predicting the surface roughness in UAT on the basis of statistical analysis of the experimental data. The surface roughness created in UAT carried out with the optimal amplitude was constantly lower than CT. This improvement in surface quality was far more significant for finishing operations.

Determination of accurate limit of cutting condition in order to obtain broken chips for various chip breaker geometries is essential to improve the machinability. This work presents a hybrid model based on the ratio of broken chip radius to the initial radius of chip to predict the type of chip regarding the characteristics of a chip breaker geometry and cutting parameters. An analytical geometrical model was developed to calculate the initial radius of chip. After running experimental tests for four types of chip breaker geometries and calculation of their chip ratio, type of chips and tool–chip contact were selected as two criteria for classifying chip ratio into three limits representing usable, acceptable, and unacceptable chips. Finally, the normalized data were used to train a neural network model to predict the type of chip which was verified by experiments carried out on a new chip breaker geometry. The trained network could predict the type of chip accurately by providing the geometrical details of the chip breaker and cutting parameters for the network.

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