Affiliations: [a] Department of Mechanical Engineering, Sri Ramakrishna Engineering College, Coimbatore-641022, Tamilnadu, India | [b] Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore-641102, Tamilnadu, India
Abstract: It is universally acknowledged that the performance of any machining process is usually evaluated in terms of its productivity and surface quality. Each of these controlling criteria are affected directly and in different way by the tool edge wear, fracture, chatter, surface roughness, cutting force etc., among these responses from machining operation, surface roughness is always considered as one of the most reliable measures for tool wear monitoring and breakage detection. This paper focuses on developing an effective, inexpensive surface recognition system which could be implemented in the modern manufacturing environments. Experiments have been conducted on Aluminium 6061 T6 and for measuring surface roughness based on Design of Experiments (DOE). A statistical multiple regression model has been developed for correlating the values obtained. For the purpose of correlation, DOE software has been used and ANOVA analyses have been carried out to identify the significant factors affecting surface roughness. The experimental values are analyzed in the optimizer technique which uses the Design of Experiment (DOE) principles to generate parameters depending on the surface roughness needed in the manufacturing environment. Thus in case of mass production, time for inspection could be reduced and hence the surface recognition system results in better performance.
