Quality Control Based Tool Condition Monitoring

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Published Oct 18, 2015
DOI https://doi.org/10.36001/phmconf.2015.v7i1.2755
Amit Kumar Jain
Reliability Engineering Lab, Discipline of Mechanical Engineering, IIT Indore, Indore MP 452020, India
Bhupesh Kumar Lad
Reliability Engineering Lab, Discipline of Mechanical Engineering, IIT Indore, Indore MP 452020, India

Abstract

Quality control and tool condition monitoring are two most important aspects of machining process. This paper studies the correlation between tool wear and surface roughness to explore the possibility of modelling the interdependencies between these two aspects. An experimental study is presented in this paper to model the relationship between product quality parameter i.e. average surface roughness and tool wear. Current study reveals that there is a strong positive correlation between surface roughness and tool wear. To map this relationship an ensemble (random forest) fault estimation model is developed for identification and estimation of cutting tool health state. The results from fault estimation model are then used to provide guidelines for future process monitoring and developing dynamic quality control policy.

How to Cite

Jain, A. K., & Lad, B. K. (2015). Quality Control Based Tool Condition Monitoring. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2755
Abstract 165 | PDF Downloads 95

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Keywords

prognosis, Tool condition monitoring, tool wear, Quality control

References
Al-jonid, K., Jiayang, W., & Nurudeen, M. (2013). A new fault classification model for prognosis and diagnosis in CNC machine. In Control and Decision Conference (CCDC), 2013 25th Chinese (pp. 3538-3543). IEEE. Altintas, Y. & Yellowley, I. (1989). In-process detection of tool failure in milling using cutting force models. Journal of Engineering for Industry, Transactions of the ASME, Vol. 111, pp.149–157, 1989. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. Colosimo, B. M., Moroni, G., & Grasso, M. (2010). Real-time tool condition monitoring in milling by means of control charts for auto-correlated data. Journal of Machine Engineering, 10. Correa, M., Bielza, C., & Pamies-Teixeira, J. (2009). Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert systems with applications, 36(3), 7270-7279. 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. 8:1073-1098. 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. Ertunc, H. M., & Oysu, C. (2004). Drill wear monitoring using cutting force signals. Mechatronics, 14(5), 533-548. Haber, R. E., Jiménez, J. E., Peres, C. R., & Alique, J. R. (2004). An investigation of tool-wear monitoring in a high-speed machining process. Sensors and Actuators A: Physical, 116(3), 539-545. Kurada S., & Bradley C., (1997). A review of machine vision sensors for tool condition monitoring. Computer in Industry, 34:55-72. 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 Annual Conference of the Prognostics and Health Management Society, pp. 1-11. Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R news, 2(3), 18-22. Lou, M. S., Chen, J. C. & Li, C. M. (1999). Surface roughness prediction technique for CNC end milling. Journal of Industry Technology, Vol. 15, No. 1, pp.1-6. Marinescu, I., & Axinte, D. A. (2008). A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. International Journal of Machine Tools and Manufacture, 48(10), 1148-1160. Montgomery, D. C. (2007). Introduction to statistical quality control. John Wiley & Sons.
Ruxu, D., Elbestawi, M. A. & Ullagaddi, B. C. (1992). Chatter detection in milling based on the probability distribution of cutting force signal. Mechanical Systems and Signal Processing, Vol. 6, No. 4:345-362. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society B, 36, 111–147. Tangjitsitcharoen, S., & Damrongthaveesak, S. (2013). Advance in monitoring and process control of surface roughness. World Academy of Science, Engineering and Technology, vol: 7 -07-29. Tansel, I. N. & McLaughlin, C. (1993). Detection of tool breakage in milling operations-I. the time series analysis approach. International Journal of Machine Tools and Manufacture, Vol. 33, No. 4, pp. 531-544, 1993. Wang, G., Yang, Y., & Li, Z. (2014). Force sensor based tool condition monitoring using a heterogeneous ensemble learning model. Sensors, 14(11):21588-21602. Wang, J., Wang, P. & Gao, R. X., (2013). Tool life prediction for sustainable manufacturing. Proceedings of the 11th Global Conference on Sustainable Manufacturing - Innovative Solutions ISBN 978-3-7983-2609-5 Universitätsverlag der TU Berlin. Wiendahl, H. P., Elmaraghy, H. A., Nyhuis, P., Zäh, M. F., Wiendahl, H. H., Duffie, N., & Brieke, M., (2007). Changeable manufacturing - classification, design, and operation. CIRP Annals - Manufacturing Technology, 56/2:783-839. Yang, K., & Jeang, A. (1994). Statistical surface roughness checking procedure based on a cutting tool wear model. Journal of Manufacturing Systems, 13(1):1-8. Zhang, D. Y., Han, Y. T. & Chen, D. C. (1995). On-line detection of tool breakages using telemetering of cutting forces in milling. International Journal of Machine Tools and Manufacture, Vol. 35, No. 1, pp.19-27. Zhong, Z. W., Zhou, J. H., & Win, Y. N. (2013). Correlation analysis of cutting force and acoustic emission signals for tool condition monitoring. In Control Conference (ASCC), 9th Asian: 1-6, IEEE. Zhou, Z. D., Chen, Y. P., Fuh, J. Y. H., & Nee, A. Y. C. (2000). Integrated condition monitoring and fault diagnosis for modern manufacturing systems. CIRP Annals - Manufacturing Technology, 49/1:387-390.
Issue
Vol. 7 No. 1 (2015): Proceedings of the Annual Conference of the PHM Society 2015
Section
Technical Research Papers

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