This study presents a new methodology to estimate tool wear rate in orthogonal cutting based on experimental data and statistical approach. In metal cutting tool wear is strongly influenced by cutting forces, speed, feed, and depth of cut. Based on these variables and cutting forces measured by dynamometer, tool wear is estimated with desired accuracy. The major objective of this study is to develop a model (equation) to predict the tool wear in orthogonal cutting by regression analysis. The work presented in this paper uses the data of conducted experiments. This data is statistically analyzed to develop a model, which can predict the wear rate of cutting tool used in orthogonal cutting operation considering different machining variables such as, spindle speed, depth of cut, feed. The cutting forces predicted by the regression analysis equation (model) is closely matching with those with results obtained experimentally. So based on another statistical equation tool wear rate is estimated over the wide range of speed, feed and depth of cut values required for different types of machining operations. The proposed methodology can be used for developing another model which will predict the tool wear rate for other machining processes.
Sonawane Swapnil Vijay, B. R. Borkar
Regression Analysis, Wear Rate, Kurtosis-based Algorithm, 3D Graphic, Geometric Tolerance
- Jorg Sohner, Blaine Lilly, Taylan Altan, "Estimation of tool wear in orthogonal cutting using the finite element analysis" Journal of Materials Processing Technology 146 (2004) 82–91
- Jaharah A. Ghani, Muhammad Rizal, Mohd Zaki Nuawi, Che Hassan Che Haron, Rizauddin Ramli, "Statistical Analysis for Detection Cutting Tool Wear Based on Regression Model"
- Deoasant P. V. "Failure Analysis of hydraulic piping system by computational approach-a survey", International Journal of Engineering and research technology, ISSN 2278-1081, vol 3, issue 12, 2014, pp. 888-891
- Kothawade V. E. "Experimental investigation and analysis for selection of rapid prototyping process" IEI, ATV Production 2016
- Ravindra Thamma, "Comparison Between Multiple Regression Models to Study Effect of Turning Parameters on the Surface Roughness", Proceedings of The 2008 IAJC-IJME International Conference ISBN 978-1-60643-379-9
- C.Z.Duan, T.Dou1, Y.J.Cai , Y.Y.Li, "Finite Element Simulation and Experiment of Chip Formation Process during High Speed Machining of AISI 1045 Hardened Steel" International Journal of Recent Trends in Engineering, Vol 1, No. 5, May 2009.
- G. H. Senussi, "Interaction Effect of Feed Rate and Cutting Speed in CNC-Turning on Chip Micro-Hardness of 304- Austenitic Stainless Steel" World Academy of Science, Engineering and Technology 28 2007.
- Suleiman Abdulkareem1, Usman Jibrin Rumah and Apasi Adaokoma; "Optimizing Machining Parameters during Turning Process", International Journal of Integrated Engineering, Vol. 3 No. 1 (2011) p. 23-27.
- Dr. S. S. Mahapatra, Amar Patnaik Prabina Ku. Patnaik, "Parametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method"
- Tugrul O zel*, Yig˘it Karpat, "Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks", International Journal of Machine Tools & Manufacture 45 (2005) 467–479.
|Published in :
||Volume 2 | Issue 3 | May-June - 2016
|Date of Publication
Cite This Article
Sonawane Swapnil Vijay, B. R. Borkar, "Estimation of Tool Wear Rate in Orthogonal Cutting Using Experimental and Statistical Approach", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.673-679, May-June-2016.
URL : http://ijsrset.com/IJSRSET1623167.php