Flank wear and I-kaz 3D correlation in ball end milling process of Inconel 718
DOI:
https://doi.org/10.15282/jmes.9.2015.7.0155Keywords:
I-kaz 3D; Flank Wear; Milling; Inconel 718; Ball End NoseAbstract
Tool wear may deteriorate the machine product quality due to high surface roughness, dimension exceeding tolerance and also to machine tool itself. Tool wear monitoring system is vital to be used in machining process to achieve high quality of the machined product and at the same time improve the productivity. Nowadays, many monitoring system developed using various sensor and statistical technique to analyze the signals being used. In this paper, I-kaz 3D method is used to analyze cutting force signal in milling process of Inconel 718 for monitoring the status of tool wear in milling process. The results from analyzing cutting force show that I-kaz 3D coefficient has a correlation with cutting tool condition. Tool wear will generate high value of I-kaz 3D coefficient than the sharp cutting tool. Furthermore, the three dimension graphical representation of I-kaz 3D for all cutting condition shown that the degree of scattering data increases with tool wear progression.
References
Rizal M, Ghani JA, Nuawi MZ, Haron CHC. A comparative study of I-kaz based signal analysis techniques: application to detect tool wear during turning process. Jurnal Teknologi. 2014;66:99–105.
Kadirgama K, Abou-El-Hossein K, Noor M, Sharma K, Mohammad B. Tool life and wear mechanism when machining Hastelloy C-22HS. Wear. 2011;270:258- 68.
Najiha MS, Rahman MM, Yusoff AR, Kadirgama K. Investigation of flow behavior in minimum quantity lubrication nozzle for end milling processes. International Journal of Automotive and Mechanical Engineering. 2012;6:768-76.
Puvanesan M, Rahman MM, Najiha MS, Kadirgama K. Experimental investigation of minimum quantity lubrication on tool wear in aluminum alloy 6061-T6 using different cutting tools. International Journal of Automotive and Mechanical Engineering. 2014;9:1538-49.
Razak NH, Rahman MM, Kadirgama K. Experimental study on surface integrity in end milling of hastelloy C-2000 superalloy. International Journal of Automotive and Mechanical Engineering. 2014;9:1578-87.
Najiha MS, Rahman MM, Yusoff AR. Modeling of the end milling process for aluminum alloy AA6061t6 using HSS tool. International Journal of Automotive and Mechanical Engineering. 2013;8:1140-50.
Abu-Mahfouz I. Drilling wear detection and classification using vibration signals and artificial neural network. International Journal of Machine Tools and Manufacture. 2003;43:707-20.
Briceno JF, El-Mounayri H, Mukhopadhyay S. Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process. International Journal of Machine Tools and Manufacture. 2002;42:663-74.
Chungchoo C, Saini D. On-line tool wear estimation in CNC turning operations using fuzzy neural network model. International Journal of Machine Tools and Manufacture. 2002;42:29-40.
Nuawi M, Nor M, Jamaludin N, Abdullah S, Lamin F, Nizwan C. Development of integrated kurtosis-based algorithm for z-filter technique. Journal of Applied Sciences. 2008;8:1541-7.
Oskoueian A, Nuawi MZ. Internal Combustion Engine Monitoring Using Strain Gauge and Analyzing With I-Kaz. IERI Procedia. 2012;1:192-8.
Rizal M, Ghani JA, Nuawi MZ, Haron CHC. The application of I-kaz TM-based method for tool wear monitoring using cutting force signal. Procedia Engineering. 2013;68:461-8.
Karim Z, Nuawi M, Ghani J, Azrulhisham E, Abdullah S. Development of machining condition monitoring system using piezoelectric sensor analyzed by I- Kaz multilevel method. World Applied Sciences Journal. 2013;21:264-8.
Ghazali M, Mansor N, Nuawi M, Ghani J. Facile wear failure monitoring of commercial bearing alloys using I-kaz method. Engineering Failure Analysis. 2013;35:545-53.
Abdullah S, Ismail N, Nuawi MZ, Nopiah ZM, Baharin MN. On the need of kurtosis-based technique to evaluate the fatigue life of a coil spring. 2009 International Conference on Signal Processing Systems. 2009. p. 989-93.
Nuawi M, Lamin F, Abdullah S, Nor M. J. M., Arifin A. Clustering of machining signal for verifying machining parameter. Proceedings of the 1st WSEAS international conference on Sensors and signals: World Scientific and Engineering Academy and Society. 2008. p. 54-9.
Aziz SAA, Nuawi MZ, Nor M, Jailani M, Daruis DDI, Bahari AR. Correlation between whole body vibratio exposure and I-KazTM 3D coefficient for Malaysian army 3-tonne truck drivers. Applied Mechanics and Materials: Trans Tech Publ; 2014. p. 156-60.
Yunoh M, Abdullah S, Saad MHM, Nopiah ZM, Nuawi MZ. Fatigue feature extraction analysis based on a K-means clustering approach. Journal of Mechanical Engineering and Sciences. 2015;8:1275-82.
Sharman A, Dewes RC, Aspinwall DK. Tool life when high speed ball nose end milling Inconel 718™. Journal of Materials Processing Technology. 2001;118:29- 35.
Gunay M, Kacal A, Turgut Y. Optimization of machining parameters in milling of Ti–6Al–4V alloy using Taguchi method, eJ. New World Science Academy Engineering Sciences. 2011;6:428-40.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2015 The Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
