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A two-way analysis of variance (ANOVA) test was carried out for surface roughness (Ra-values), flexural Young's moduli, and maximum force values, followed by post hoc multiple comparison tests.
Fig. 11 Response optimization plot for surface roughness (Ra).
Fig. 2 Main effects plot for surface roughness (Ra).
RSM optimization result for surface roughness (Ra) is shown in Fig. 11 and Table 6.
It is found that successful brazing and good wetting can be achieved by the least voids by using an average surface roughness (Ra value) for the base material.
Mathematical models have been created for surface roughness, namely Ra and Rz, through response surface methodology (RS M.
The optimum cutting condition for surface roughness are in the region of cutting speed (v) = 220 m/min, feed (f) = 0.087 mm/rev and depth of cut (d) = 0.52 mm, which resulted the optimized surface roughness value (Ra) of 0.447 μm.
The mean value of surface roughness parameters (Ra) and root-mean-square (RMS) height of the samples were listed in Table 1.
In the present works, the R2 values for the surface roughness (Ra) and flank wear (VB) are 87% and 96% respectively, which reveals statistical significance of the model and the goodness fit for the model.
The results show that typically referenced average values for the surface roughness such as Ra and Rz may prove insufficient to reliably predict the wear behavior of the coating.
The average values of surface roughness (Ra) were subjected to the D'Agostino-Pearson test for normality; then to a two-way ANOVA (bonding materials × time), and the means were compared using the Bonferroni test (α = 0.05).
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