OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology Ta b l e 3 Roller burnishing experimental matrix Cutting speed (V) (rpm) Feed (f) (mm/rev) No. of passes Surface roughness (Ra) (µm) Microhardness (HV) Roundness error (Re) (µm) 300 0.2 3 0.81 117 7.7 200 0.15 2 0.82 114 9.6 200 0.15 4 0.89 116 8.6 200 0.25 2 0.92 116 5.4 200 0.25 4 0.9 125 8.7 400 0.15 2 0.94 118 10.1 400 0.15 4 0.84 111 1.6 400 0.25 2 0.97 110 8.4 400 0.25 4 0.79 113 2.9 300 0.2 3 0.81 117 8.4 300 0.2 3 0.81 117 8.6 100 0.2 3 0.92 112 13.2 500 0.2 3 0.93 104 4.2 300 0.1 3 0.94 123 1.5 300 0.3 3 0.96 124 2 300 0.2 1 0.95 123 8.7 300 0.2 5 0.86 125 4 300 0.2 3 0.83 117 6.9 300 0.2 3 0.82 113 8.3 300 0.2 3 0.81 118 8.7 the model. Adequate precision is a measure of the range in predicted response about its associated error, in other words a signal-to-noise ratio. Its desired value is 4 or more. The ANOVA results for surface roughness, microhardness, and roundness error when roller burnishing a workpiece is given in Table 4. ANOVA results for surface roughness show, that the model F-value is 46.91 which implies that the model is significant. The “Prob > F” values less than 0.0500 that the model terms are significant. In this case f, N, V×f, V×N, f×N, V2, f2, N2 are the significant model terms. The ANOVA results for microhardness show that the model F value is 11.99, which means the model is significant. The probability that such a large “Model F-Value” could be caused by noise is only 0.03 %. In this case, V, V×f, V×N, f×N, V2, f2, N2 are the significant model terms. The results of microhardness analysis obtained by ANOVA show that the “Model F-Value” of 17.62, which means that the model is significant. In this case, V, N, V×N, f×N, f2 are significant model terms. The R-squared values, which measure the variation proportion in the data points, are above 0.9 for all the developed models. Therefore, the developed empirical equations are reliable to predict the surface roughness, microhardness, error in roundness during the roller burnishing of Al6061-T6 alloy (Eq. 1 to 3). For better understanding, two-dimensional (2-D) plots are plotted by varying the cutting speed, feed, and number of passes using the developed Eq. 1 to 3, respectively. Curves showing the surface roughness, microhardness, and roundness error are plotted by varying one of the input parameters and keeping the other parameters constant. Fig. 2a shows the change of the measured characteristics as a function of the cutting speed plotted using a feed value of 0.2 mm/rev and three passes. It can be seen that surface roughness decreases with an increase in the cutting speed up to 360–380 rpm and then increases. Microhardness can be seen as increasing with the cutting speed. However, there is an optimum, and it can be regarded as
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