An enhancement of integrated fuzzy-topsis to improve machining surface roughness

Abstract

Machining is defined as a process to remove material in the form of chips using single or multiple wedge-shaped cutting tools to produce the desired shape. This process has successfully produced a closer dimensional accuracy and surface finish to meet the industrial demands. However, it is difficult to find the optimal machining parameter values that yield the minimum surface roughness (Ra) values to meet technical specifications for end milling and laser assisted machining (LAM). Thus, this research proposed the integration of Fuzzy Logic (FL) and Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) to predict minimum Ra values and find the optimal machining parameters. In the proposed Fuzzy-TOPSIS model, initially FL is used to consider correct membership functions, linguistic terms and rules. Then, TOPSIS uses the weighted values obtained to handle instabilities in FL with advanced inference methods and rank the FL results by applying the obtained fuzzy intervals. The integration of Fuzzy-TOPSIS model has successfully reduced Ra values by 0.066µm for end milling and 0.112µm for LAM. Upon achieving the minimum values, a precise combination of optimal machining parameters can be obtained. These results reveal that the Fuzzy-TOPSIS model is capable of improving the quality of finished products during machining processes.