Open AccessPrediction of Surface Roughness using Sensor Fusion Regression Model
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l1134.10812s19
Subject(s) - machining , surface roughness , surface finish , mechanical engineering , fuse (electrical) , regression analysis , fusion , quality (philosophy) , surface (topology) , predictive modelling , materials science , cutting tool , computer science , engineering , machine learning , composite material , mathematics , geometry , linguistics , philosophy , electrical engineering , epistemology
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