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 prioritization of equipment is among the decisions of great interest in maintenance management, given the effects it reflects on numerous sub-functions and the dependence it implies on various factors. The mastery of the techniques in this context is gaining an increasing importance, especially in heavy industries operating multiple production lines. According to the literature, the Analytic hierarchy process (AHP) method is among the most common techniques to resolve this problem, despite the concerns it involves. Knowing that, this technique supports two synthesis modes: distributive and ideal, and a confusing conflict is noticed; although the second mode seems theoretically more adapted to this problem, the first dominates in the practical aspect. In response to this conflict, the objective of this work is to demonstrate that the ideal synthesis mode is more suitable, through a comparative approach within this context. An improved AHP-approach is implicitly proposed within the study.
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