Review of Tool Condition Monitoring Methods

Visariya, Ramesh; Ruparel, Ronak; Yadav, Rahul Kumar

doi:10.5120/ijca2018916853

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…However, the relationship function developed by the statistical way is often a linear model. Therefore, it is unable to explain the non-linearity between dependent and independent variables [17]. These linear statistical models were developed in conjunction with off-process TCM systems.…”

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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“…Due to it's importance in the manufacturing industry, TCM has been an active area of research for the past few decades [16][17][18]. In particular, TCM for machining [19][20][21][22][23][24] and forming [25][26][27] processes have received a lot of attention.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Online TCM systems are essential in manufacturing industry for cost reduction and quality improvement [22]. Some of the methods are based on mathematical models [18] while others are based on statistical methods [17,22]. [22] summarizes various TCM methods for the milling process that have been practiced and described in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning

Nazir

Shao

2021

Journal of Manufacturing Processes

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“…Tool wear can significantly impact the machining process of a part, reducing the quality of the surface finish and potentially damaging the part, resulting in either additional rework or the need to scrap the part [3]. Research into Tool Condition Monitoring (TCM) systems, where sensing, decision making and control strategies are introduced to detect the onset of tool wear, has therefore attracted a significant amount of interest from the academic community with a number of review articles listed [1][4] [5][6] [7] [8]. However, key challenges remain, including the development of TCMs which can be cost effective to install and maintain and are robust and reliable in the presence of the variability and discontinuities of real-world machining.…”

Section: Introductionmentioning

confidence: 99%