Development of Tool Wear Machining Monitoring Using Novel Statistical Analysis Method, I-kaz™

Ahmad, Muhamad Arif Fadli; Nuawi, Mohd. Zaki; Abdullah, Shahrum; Wahid, Zaliha; Karim, Z.; Dirhamsyah, M.

doi:10.1016/j.proeng.2015.02.043

Cited by 32 publications

(14 citation statements)

References 15 publications

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“…Wear mechanisms may coincide, or one of them may dominate the process. These mechanisms can lead to several types of wear [9,22,23]. However, two types of them which called crater and flank wear are most distinguished.…”

Section: Introductionmentioning

confidence: 99%

Cutting tool wear progression index via signal element variance

Kasim

Nuawi

Ghani

et al. 2019

JMES

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This paper presents a new statistical-based method of cutting tool wear progression in a milling process called Z-rotation method in association with tool wear progression. The method is a kurtosis-based that calculates the signal element variance from its mean as a measurement index. The measurement index can be implicated to determine the severity of wear. The study was conducted to strengthen the shortage in past studies notably considering signal feature extraction for the disintegration of non-deterministic signals. The Cutting force and vibration signals were measured as a tool of sensing element to study wear on the cutting tool edge at the discrete machining conditions. The monitored flank wear progression by the value of the RZ index, which then outlined in the model data pattern concerning wear and number of samples. Throughout the experimental studies, the index shows a significant degree of nonlinearity that appears in the measured impact. For that reason, the accretion of force components by Z-rotation method has successfully determined the abnormality existed in the signal data for both force and vibration. It corresponds to the number of cutting specifies a strong correlation over wear evolution with the highest correlation coefficient of R2 = 0.8702 and the average value of R2 = 0.8147. The index is more sensitive towards the end of the wear stage compared to the previous methods. Thus, it can be utilised to be the alternative experimental findings for monitoring tool wear progression by using threshold values on certain cutting condition.

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Section: Introductionmentioning

confidence: 99%

Cutting tool wear progression index via signal element variance

Kasim

Nuawi

Ghani

et al. 2019

JMES

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“…Figure 1. Details of the I-kaz signal decomposition process [13] Previously, the I-kaz™ method has been successfully applied for the analysis of car engine bearings [14], engine blocks [15], suspension systems [16], machine cutting tools [17], etc. To the author's best knowledge, this method has never been applied in the field of induction motor bearing diagnostics.…”

Section: Integrated Kurtosis-based Algorithm For Z-filter (I-kaz™) Mementioning

confidence: 99%

Induction motor bearing fault diagnostics using i-kaz™ and decision tree classification

Othman

Nuawi²,

Mohamed³

2016

Int. J. Automot. Mech. Eng.

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In this paper, the I-kaz™ method is proposed for the detection of induction motor bearing faults using vibration signals, which has not been presented so far. The purpose of the study is to compare this new technique with the classical kurtosis method in the time domain and to validate the performance of the proposed I-kaz coefficient using a decision tree classification. Three bearing conditions are investigated; i.e. normal, ball fault and inner race fault; with a very small fault size (0.1778 mm). All faulty bearings are artificially damaged using electro-discharge machining and placed in the motor drive end side. The experimental test rig consists of a 2 HP induction motor, a torque transducer, a dynamometer and control electronics. Vibration data is obtained using an accelerometer and analyzed using MATLAB software for the time domain analysis which include the Ikaz graphical and coefficient comparison with time waveform and kurtosis values for all bearing conditions. Then, both features are used to train a conditional inference tree (CTree) fault classifier separately. The proposed I-kaz coefficient provides higher percentage differences between all faulty and normal bearings compared to kurtosis. However, the I-kaz graphic presents similar identification as the time waveform where only the inner race fault is distinguished from the normal bearing. The training classification results also revealed that the I-kaz coefficient is significantly better with an accuracy of 99.64% and a Kappa value of 0.9946 compared to kurtosis at only 63.57% and 0.4536, respectively. Furthermore, all test data were classified accordingly using the I-kaz coefficient whereas for kurtosis, only 65% are correctly classified with the 0.475 Kappa value. It is proved that the I-kaz™ method is suitable for induction bearing fault detection and is recommended as a classification feature, especially for the diagnostics of ball fault which is the most difficult to diagnose.

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“…This sensor is very good to be used in a robust machining condition, such as protection against splashes, moisture, resistance to other objects and debris [8,9]. However, vibration can also be measured using piezoelectric films in the unit of millimeter or micrometer [10][11]. The small and flexible film size makes it easy to attach to a limited or small area such as a tool holder during machining process.…”

Section: Introductionmentioning

confidence: 99%

Vibration–Based online Tool Wear Monitoring Using Piezo Film Sensor Analyzed by I-kaz Multilevel Signal Feature

Karim

Hussin

Yasir

et al. 2019

IJEAT

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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, 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. 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.

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Development of Tool Wear Machining Monitoring Using Novel Statistical Analysis Method, I-kaz™

Cited by 32 publications

References 15 publications

Cutting tool wear progression index via signal element variance

Cutting tool wear progression index via signal element variance

Induction motor bearing fault diagnostics using i-kaz™ and decision tree classification

Vibration–Based online Tool Wear Monitoring Using Piezo Film Sensor Analyzed by I-kaz Multilevel Signal Feature

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