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Title: Experimental study to predict of tool wear in dry turning of EN 24 steel using design of experiment and verification through ANOVA and RSM
Authors: G. Ragul, S. Sankar, Arun Thampi, Tedy Thomas, Praveen Maruthur, Praveen C. P.
Journal: ARPN Journal of Engineering and Applied Sciences
Publisher: Khyber Medical College, Peshawar
Country: Pakistan
Year: 2016
Volume: 11
Issue: 16
Language: English
This research work reports the significance of influence of speed, feed and depth of cut on tool wear. In this study an experiments was carried out in kirloskar master 35- Lathe using work tool made up of ceramic with an Al2O3+TiC matrix and the work material is EN24 steel of hardness 48 HRC. Also, an attempt was made to fuse cutting force, cutting temperature and tool vibration (displacement), along with cutting velocity, feed and depth of cut to predict tool wear. In this work cutting force were measured by Kistler force dynamometer, cutting temperature were measured by Infra-red thermometer, tool vibration were measured by piezoelectric digital vibrometer and tool wear were measured by optical microscope. By Minitab software which is best tool for optimizing the cutting parameters such as cutting velocity, feed and depth of cut. The above study said parameters are optimized using DoE. The optimized cutting parameters using Taguchi method (L18 Mixed Design) were compared with Analysis of Variance (ANOVA) and Response Surface Methodology (RSM). In addition the results were verified with manual method for any deficiency. The above study revealed that the results obtained from ANOVA and RSM is closely matching with the results obtained from DoE.
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