Article: ANN assisted sensor fusion model to predict tool wear during hard turning with minimal fluid application Journal: International Journal of Machining and Machinability of Materials (IJMMM) 2013 Vol.13 No.4 pp.398 - 413 Abstract: Accurate prediction of tool wear can be made possible if factors like cutting force, cutting temperature, acoustic emission signals and vibration signals are used effectively and collectively. Each of these factors predicts tool wear in their own characteristic fashion - high cutting temperature is an index of flank wear and crater wear, whereas variation in cutting force indicates fracture type of tool failure more effectively. Even though each of these factors can be used individually, a more accurate prediction will be possible by considering the indices of tool wear collectively rather than individually. In the present work, an attempt was made to fuse cutting force, cutting temperature and displacement of tool vibration along with cutting velocity, feed and depth of cut to predict tool wear during turning of AISI 4340 steel of 46 HRC with minimal fluid application using hard metal insert with sculptured rake face. A regression and an ANN model were developed to fuse the cutting force, cutting temperature and displacement of tool vibration signals to predict tool flank wear. From the results, it was observed that the model based on ANN was found to be superior to the regression model in its ability to predict tool wear. Inderscience Publishers - linking academia, business and industry through research

Title: ANN assisted sensor fusion model to predict tool wear during hard turning with minimal fluid application

Authors: P. Sam Paul; A.S. Varadarajan

Addresses: School of Mechanical Sciences, Karunya University, Coimbtore, 641114, Tamil Nadu, India ' Department of Mechanical Engineering, NSS College of Engineering, Palakkad, 678008, Kerala, India

Abstract: Accurate prediction of tool wear can be made possible if factors like cutting force, cutting temperature, acoustic emission signals and vibration signals are used effectively and collectively. Each of these factors predicts tool wear in their own characteristic fashion - high cutting temperature is an index of flank wear and crater wear, whereas variation in cutting force indicates fracture type of tool failure more effectively. Even though each of these factors can be used individually, a more accurate prediction will be possible by considering the indices of tool wear collectively rather than individually. In the present work, an attempt was made to fuse cutting force, cutting temperature and displacement of tool vibration along with cutting velocity, feed and depth of cut to predict tool wear during turning of AISI 4340 steel of 46 HRC with minimal fluid application using hard metal insert with sculptured rake face. A regression and an ANN model were developed to fuse the cutting force, cutting temperature and displacement of tool vibration signals to predict tool flank wear. From the results, it was observed that the model based on ANN was found to be superior to the regression model in its ability to predict tool wear.

Keywords: hard turning; tool wear; artificial neural networks; ANNs; regression analysis; sensor fusion; minimal fluid application; wear prediction; flank wear; cutting force; cutting temperature; displacement; tool vibration; cutting speed; cutting velocity; feed; depth of cut.

DOI: 10.1504/IJMMM.2013.054263

International Journal of Machining and Machinability of Materials, 2013 Vol.13 No.4, pp.398 - 413

Received: 01 Aug 2011
Accepted: 27 Nov 2011
Published online: 26 Dec 2013 *

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Title: ANN assisted sensor fusion model to predict tool wear during hard turning with minimal fluid application

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