Prediction of Surface Roughness using Sensor Fusion Regression Model
Anuja Beatrice B1, Leo Dev Wins K2, Ebenezer Jacob Dhas D S3, Arul Kirubakaran D4
1Anuja Beatrice B, Sri Krishna Arts and Science College, Coimbatore (Tamil Nadu), India.
2Leo Dev Wins K, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
3Ebenezer Jacob Dhas D S, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
4Arul Kirubakaran D, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
Manuscript received on 08 October 2019 | Revised Manuscript received on 22 October 2019 | Manuscript Published on 26 December 2019 | PP: 538-540 | Volume-8 Issue-12S October 2019 | Retrieval Number: L113410812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1134.10812S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Surface roughness decides the quality of machined components during machining processes. Output parameters namely cutting temperature, cutting force, tool wear, vibration etc. have direct influence on surface roughness of machined components. It is anticipated that better prediction would be possible if the above mentioned parameters are collectively considered with machining parameters. In this investigation, an effort was made to fuse machining parameters with cutting temperature to predict surface roughness while machining H13 steel. The developed regression model was tested for its ability to predict surface quality. The results proved that the developed sensor fusion regression model can be used for better prediction of cutting performance.
Keywords: Regression Analysis, Hard Turning, Minimal Cutting Fluid Application, Surface Roughness, Sensor Fusion.
Scope of the Article: Regression and Prediction