Surface roughness is a very important measurement in machining process and a determining factor describing the quality of machined surface. This research aims to analyse the effect of cutting parameters [cutting speed (v), feed rate (f) and depth of cut (d)] on the surface roughness in turning process. For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. The ANN model shows a good correlation between the predicted and the experimental surface roughness values, which indicates its validity and accuracy. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.
The quality of surface roughness for machined parts is essential in the manufacturing process. The cutting tool plays an important role in the roughness of the machined parts. The process of determining the number of tolerant faults is problematic; this is due to the fact that the behaviour of the cutting tool is random. In this paper, we use an approach based on order statistics to study the construction of functional and reliability characteristic for the faults tolerant machined parts in each five batch of ten machined parts. Our experiments show that the number of faulty machined parts will not exceed two and the distribution of the minimum gives the best interval of the surface roughness. We have shown that the distribution of extreme order statistics plays an important role in determining the lower and upper limits of the roughness measurements depending on the reliability of the cutting tool.
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