Intelligent Monitoring of Surface Integrity and Cutter Degradation in High-speed Milling Processes
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Abstract
In high-speed milling process, dynamic monitoring and detection of work-piece surface defects and cutter degradation is a very important and also an extremely difficult task. Due to the inconsistency and variability of cutter geometry/dimensions, the uncertainties of machine tool conditions, as well as the complexity of the cutting process itself, the modelling of cutting performance in high-speed milling process has remained a challenging issue for both academia and industry. This paper attempts to exploit a force-based approach to model the cutting performance and detect the surface integrity of high-valued work-pieces in high-speed milling process. Experiments on high-speed dry-milling of Titanium (Ti6Al4V) using ball-nose end mills were conducted to verify the proposed approach. Preliminary findings from the study have shown that the force-based modelling techniques proposed is able to establish the association between cutting force signals and the degradation of cutting performance and so as to eliminate surface defects of work-pieces.
How to Cite
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fault diagnosis, Intelligent monitoring, Performance modelling, Cutter degradation, High-speed milling, Surface integrity
Baker, M. (2005) Some Aspects of High-speed Chip Formation, in Proceedings of 8th CIRP International Workshop on Modeling of Machining Operations, Chemnitz, Germany, pp. 101.
Chen, M., Sun F., Wang, H., Yuan, R., Qu, Z. & Zhang, S. (2003) Experimental Research on the Dynamic Characteristics of the Cutting Temperature in the Process of High-speed Milling, Journal of Materials Processing Technology, vol. 138, pp. 468–471.
Dimla, Sr.D.E. & Lister, P.M. (2000) On-line Metal Cutting Tool Condition Monitoring: I: Force and Vibration Analyses, International Journal of Machine Tools and Manufacture, vol. 40, no. 5, pp. 739-768.
Dimla, Sr.D.E. (2000) Sensor Signals for Tool-wear Monitoring in Metal Cutting Operations - A Review of Methods, International Journal Machine Tools and Manufacture, vol. 40, no. 8, pp. 1073-1098.
Ekinovic, S., Dolinsik, S. & Jawahir, I.S. (2004) Some Observations of the Chip Formation Process and the White Layer Formation in High-speed Milling of Hardened Steel, Machining Science and Technology, vol.8, no. 2, pp. 327-340.
Ertekina, Y.M., Kwon, Y. & Tseng, T.L. (2003) Identification of Common Sensory Features for the Control of CNC Milling Operations under Varying Cutting Conditions, International Journal Machine Tools and Manufacture, vol. 43, no. 9, pp. 897-904.
Haber, R.E., Jiménez, J.E., Peres, C.R. & Alique, J.R. An Investigation of Tool-wear Monitoring in a High-speed Machining Process, Sensors and Actuators A: Physical, vol. 116, no. 3, pp. 539-545.
Hortig, C. & Svendsen, B. (2007) Simulation of Chip Formation during High-speed Cutting, Journal of Materials Processing Technology, vol. 186, no. 1-3, pp. 66-76.
Huang, S., Goh, K.M., Shaw, K.C., Wong, Y.S. & Hong, G.S. (2007) Model-based Monitoring and Failure Detection Methodology for Ball-nose End Milling, in Proceedings of 12th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Patras, Greece, pp. 155-160.
Kim, S.W., Lee, C.M., Lee, D.W., Kim, J.S. & Jung, Y.H. (2001) Evaluation of the Thermal Characteristics in High-speed Ball-end Milling, Journal of Materials Processing Technology, vol. 113, no. 1-3, pp.406-409.
Li, X., Lim, B.S., Zhou, J.H., Huang, S., Phua, S.J., Shaw, K.C. & Er, M.J. (2009) Fuzzy Neural Network Modelling for Tool-wear Estimation in Dry Milling Operation, in Proceedings of 2009 Annual Conference of Prognostics and Health Management (PHM 2009), San Diego, USA, Online Conference Proceeding, phmc0968.
Marinescu, I. & Axinte, D.A. (2008) A Critical Analysis of Effectiveness of Acoustic Emission Signals to Detect Tool and Work-piece Malfunctions in Milling Operations, International Journal of Machine Tools and Manufacture, vol. 48, no. 10, pp. 1148-1160.
Ning, Y., Rahman, M. & Wong, Y.S. (2001) Investigation of Chip Formation in High-speed End Milling, Journal of Materials Processing Technology, vol. 113, no. 1-3, pp. 360–367.
Ning, Y., Rahman, M. & Wong, Y.S. (2000) Monitoring of Chatter in High-speed End Milling Using Audio Signals Method, in Proceedings of the 33rd International MATADOR Conference, pp. 421- 426. Manchester, UK.
Orhan, S., Er, A.O., Camuşcu, N. & Aslan, E. (2007) Tool-wear Evaluation by Vibration Analysis during End Milling of AISI D3 Cold Work Tool Steel with 35 HRC Hardness, NDT & E International, vol. 40, no. 2, pp. 121-126.
Özel T. & Altan, T. (2000) Process Simulation Using Finite Element Method - Prediction of Cutting Forces, Tool Stresses and Temperatures in High- speed Flat End Milling, International Journal Machine Tools and Manufacture, vol. 40, no. 5, pp.713-738.
Toh, C.K., (2004) Static and Dynamic Cutting Force Analysis when High-speed Rough Milling Hardened Steel, Materials & Design, vol. 25, no. 1, pp. 41-50.
Torabi, A.J., Er, M.J., Li, X., Lim, B.S., Zhai, L.Y., Phua, S.J., San, L., Huang S. & Tijo, J.T.T. (2009) A Survey on Artificial Intelligence Technologies in Modelling of High-speed End-milling Processes, in Proceeding of 2009 IEEE International Conference on Advanced Intelligent Mechatronics, Singapore,pp. 320-325.
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