Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method

Response surface and neural network based predictive models of cutting temperature in hard turning

“…The increased hardness of material gives rise to the restraining force [27] and supposedly rises the cutting temperature. Even though the application of coolant at high pressure reduces the cutting temperature and provides the lubrication [18], the change of hardness from 40 HRC to 48 HRC and then finally to 56 HRC originates different amounts of restraining force within the material and exerts severe effect on determining the cutting temperature.…”

“…The calibration setup and equipments of the thermocouple are shown in Fig. 3(a) [18]. The chip of AISI 1060 steel (work material) and tungsten carbide (tool material) was joined to create the junctions of the thermocouple.…”

Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network

“…The surface roughness parameter is found, in Fig. 10 ) also suggests that the average surface roughness is expected to increase as square of feed rate for a constant nose radius of tool [28]. From the definition of feed in turning, it is understandable that a higher feed is reflected by a broader helicoidal groove over the machined surface which escalates the magnitude of surface roughness.…”

Analysis of dissimilar friction stir welding process for tensile properties of EN AW 2024 and EN AW 5083

“…Starting from this approach, the quality criteria selected for calculation of the signal to noise ratio is the higher the better criteria. Thus, both the ultimate tensile strength and percentage of elongation can be maximized using the following formula [24]. Figure 9.…”