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Author Naidu, Konanki M. ♦ Rao, Sadineni Rama
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Surface Roughness ♦ Surface Roughness Prediction ♦ Machining Process ♦ Ten Experimental Test Case ♦ Developed Model ♦ Carbide Tool ♦ Predominant Machining Criterion ♦ Present Work ♦ Machining Parameter ♦ Artificial Neural Network ♦ Vital Role ♦ Linear Regression Equation ♦ Taguchi Experimental Design Methodology
Abstract Surface roughness is the predominant machining criteria in any machining process and plays a vital role in manufacturing industries. The present work focused on the modeling of surface roughness in turning of AA 6351 alloy with carbide tool. Cutting speed, feed and depth of cut were considered as machining parameters and surface roughness was considered as the response. Experiments were conducted to develop the linear regression equations based on Taguchi’s experimental design methodology. Moreover, Artificial Neural Network (ANN) model was also developed for the surface roughness. Further, the performance of the developed model has been tested with the help of ten experimental test cases.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study