Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Authors: Kious Mecheri, Hadjadj Abdechafik, Ameur Aissa

Abstract:

The wear of cutting tool degrades the quality of the product in the manufacturing processes. The on line monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear on line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc…. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions.

Keywords: Flank wear, cutting forces, high speed milling, signal processing, neural network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091414

References:

[1] Bin Li, A Review of Tool Wear Estimation Using Theoretical Analysis and Numerical Simulation Technologies, Int Journal of Refractory Metals and Hard Materials, 35, (2012), 143-151.
[2] M. A. Elbestawi, T. A. Papazafiriou and R. X. Du, In-Process Monitoring of Tool Wear in Milling Using Cutting Force Signature, International Journal of Machine Tools & Manufacture 31 (1991) 55-73.
[3] Y. Altintas, I. Yellowley, In-Process Detection of Tool Breakage in Milling Using Cutting Force Models, Journal of Engineering for Industry 111 (1988) 149–157.
[4] Sarhan, et al, Interrelationships between Cutting Force Variation and Tool Wear in End-Milling’ Journal of Materials Processing Technology 109 (2001), 229-235.
[5] M. Kious, A. Ouahabi, M. Boudraa, R. Serra, Detection process Approach of Tool Wear in High Speed Milling , Measurement, Volume 43, Issue 10, ( 2010),1439-1446.
[6] P. Bhattacharyya, D. Sengupta, S. Mukhopadhyay , Cutting Force-Based Real-Time Estimation of Tool Wear in Face Milling Using a Combination of Signal Processing Techniques, Mechanical Systems and Signal Processing, 21, (2007),2665-2683.
[7] J.A. Ghani, M. Rizal, M.Z. Nuawi, M.J. Ghazali, C.H.C. Haron, Monitoring Online Cutting Tool Wear Using Low-Cost Technique and User-Friendly GUI, Wear, 271, (2011), 2619-2624.
[8] S.L Chen, Y.W. Jen, Data Fusion Neural Network for Tool Condition Monitoring in CNC Milling Machining, Int. J. Machine Tools & Manufacture 40 (2000),, 381–400.
[9] H. Saglam, A. Unuvar, Tool Condition Monitoring in Face Milling Based On Cutting Forces by a Neural Network, International Journal of Production Research 41 (2003), 1519–1532.
[10] N. Ghosh et al, Estimation of Tool Wear during CNC Milling Using Neural Network-Based Sensor Fusion, Mechanical System and Signal Processing 21 (2007), 466–479.