I-Kaz^TM-Based Analysis of Cutting Force Signals for Tool Condition Monitoring in Turning Process

Abstract:

Cutting force is an important signal in machining process and has been widely used for tool condition monitoring. Monitoring the condition of the cutting tool in the machining process is very important to maintain the machined surface quality and consequently reduce inspection costs and increase productivity. This paper utilizes I-kaz-based analysis of cutting force signal to monitor the status of tool wear. The cutting force signals are measured by two channels of strain gauge that were mounted on the surface of tool holder. Experiments were carried out by turning hardened carbon steel and cutting force signals were analyzed using I-kaz^TM technique by integrating two component of signals (I-kaz 2D, Z₂), I-kaz of cutting force (Z of F_y), and I-kaz of feed force (Z of F_x). The results show that I-kaz of feed force can be effectively used to monitor tool wear progression during turning operation.

• Periodical: Applied Mechanics and Materials (Volume 471)
• Pages: 203-207
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.471.203
• Online since: December 2013
• Authors: Muhammad Rizal, Jaharah A. Ghani, Mohd Zaki Nuawi, Che Hassan Che Haron
• Keywords: Cutting Force, I-Kaz^TM Method, Low-Cost Sensor, Tool Condition Monitoring

References

[1] D.E. Dimla, P.M. Lister, On-line metal cutting tool condition monitoring. I: force and vibration analyses, Inter. J. Mach. Tools Manufact. 40 (2000) 739-768.

DOI: 10.1016/s0890-6955(99)00084-x

[2] K. Jemielniak, L. Kwiatkowski, P. Wrzosek, Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network, J. Intell. Manuf. 9 (1998) 447-455.

[3] S. Das, A.B. Chattopadhyay, A.S.R. Murthy, Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach, Neural Networks, 9 (1996) 1639-1645.

DOI: 10.1016/s0893-6080(96)00036-6

[4] C. Chungchoo, D. Saini, On-line tool wear estimation in CNC turning operations using fuzzy neural network model, Inter. J. Mach. Tools Manufact. 42 (2002) 29–40.

DOI: 10.1016/s0890-6955(01)00096-7

[5] S. Abdullah, N. Ismail, M.Z. Nuawi, Z.M. Nopiah, M.N. Baharin, On the need of kurtosis-based technique to evaluate the fatigue life of a coil spring, in: International Conference on Signal Processing Systems, IEEE, 2009, pp.989-993.

DOI: 10.1109/icsps.2009.181

[6] N.I.I. Mansor, M.J. Ghazali, M.Z. Nuawi, S.E.M. Kamal, Monitoring bearing condition using airborne sound, Inter. J. Mech. Mater. Eng. 4(2009) 152-155.

[7] M.Z. Nuawi, F. Lamin, M.J.M. Nor, N. Jamaluddin, S. Abdullah, C.K. e. Nizwan, Integration of I-kaz Coefficient and Taylor Tool Life Curve for Tool Wear Progression Monitoring in Machining Process, Inter. J. Mech. 4 (2007) 44-50.

[8] J.A. Ghani, M. Rizal, M.Z. Nuawi, C.H.C. Haron, M.J. Ghazali, M.N.A. Rahman, Online cutting tool wear monitoring using I-kaz method and new regression model, Adv. Mater. Res. 126-128 (2010) 738-743.

DOI: 10.4028/www.scientific.net/amr.126-128.738

[9] 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.

DOI: 10.1016/j.wear.2011.01.038

[10] J.A. Ghani, M. Rizal, M.Z. Nuawi, C.H.C. Haron, Development of an Adequate Online Tool Wear Monitoring System in Turning Process using Low Cost Sensor, Adv. Sci. Lett. 13 (2012) 702-706.

DOI: 10.1166/asl.2012.3939

[11] M.Z. Nuawi, F. Lamin, M.J.M. Nor, N. Jamaluddin, S. Abdullah, C.K.E. Nizwan, Development of integrated kurtosis-based algorithm for z-filter technique, J. Appl. Sci. 8 (2008) 1541-1547.

DOI: 10.3923/jas.2008.1541.1547