Vibration–Based online Tool Wear Monitoring Using Piezo Film Sensor Analyzed by I-kaz Multilevel Signal Feature
Z. Karim1, Azman Hussin2, Ahmad Yasir M. S3, M. Z. Nuawi4
1Z. Karim, Department of Mechanical and Manufacturing, University Kuala Lumpur Malaysia France Institute, Bandar Baru Bangi, Malaysia.
2Azman Hussin, Department of Mechanical and Manufacturing, University Kuala Lumpur Malaysia France Institute, Bandar Baru Bangi, Malaysia.
3Ahmad Yasir M. S., Department of Mechanical and Manufacturing, University Kuala Lumpur Malaysia France Institute, Bandar Baru Bangi, Malaysia.
4M. Z. Nuawi, Department of Mechanical and Material Engineering, University Kebangsaan Malaysia, Bandar Baru Bangi, Malaysia.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3397-3403 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9509088619/2019©BEIESP | DOI: 10.35940/ijeat.F9509.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wear in cutting tool is a normal phenomenon in machining process. Problems such as dimensional precision, surface finish quality and defect cost is due to the wear. The wear also can cause unexpected stop time and lower down the manufacturing productivity. Therefore, a system to monitor the progression of the tool wear is needed to predict the wear status and stop machining operation once the wear reach the allowed limit. In this study, the monitoring system was designed by utilizing 2 units of piezoelectric film sensors which are capable of detecting and analyzing signals related to tool holder vibration during the machining process in both feed and tangential axes. The sensors were stacked on x axis and z axis surfaces of the tool holder and signals were channeled to a charge amplifier and then to the digital data acquisition equipment which then display the vibration signal in time domain on the computer screen. A total of 8 experiments were carried out using CNC turning machine. Vibration signal and wear measurement were recorded for each run in the experiment. The experiment were stopped once the wear reach approximately 0.3mm. I-kaz multilevel signal features were extracted from the vibration signal recorded and then correlated with the flank wear status. There is a solid or substantial correlation between the cutting tool wear condition and I-kaz multilevel coefficient value, with average of 0.87 for I-kaz x coefficient (tangential direction) and 0.910 for I-kaz z coefficient (feed direction). This affirm that I-kaz multilevel signal feature can be assigned as the input criterion for the tool condition monitoring system that can estimate the current status of the flank wear on the cutting tools which can prevent defect in the machining process.
Keywords: Tool condition monitoring system, piezo electric film, wear prediction, I-kaz, and correlation.