A review of machine vision sensors for tool condition monitoring

Kurada, S.; Bradley, Colin

doi:10.1016/s0166-3615(96)00075-9

Cited by 354 publications

(135 citation statements)

References 36 publications

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“…Temperature measurement in machining has been extensively reviewed by Davies et al [55]. The use of vision systems for monitoring tool condition has been comprehensively reviewed by Kurada and Bradley [56]. As in many applications using machine vision, object illumination notably impacts the process and for industrial applications can lead to process unreliability.…”

Section: Other Sensor Typesmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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Section: Other Sensor Typesmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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“…Statistically, 20% of the down-time of these machines is attributed to the cutting tool failure, resulting in productivity and economic losses [44]. Thus, the prediction of the amount of wear before reaching the wear limit of the cutters may help improving the reliability, the availability and the safety of CNC machines, while insuring the surface roughness requirements and reducing the maintenance costs.…”

Section: Application and Simulation Resultsmentioning

confidence: 99%

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Diego

Medjaher

Zerhouni

2012

Mechanical Systems and Signal Processing

198

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The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.

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“…The majority of the research work has pursued only the measurement of flank wear whereas few researchers have attempted to measure both flank and crater wear. Flank wear regions can be imaged with a CCD camera; however, crater wear determination requires the projection of a structured light pattern onto the tool, in order to derive depth information from within the crater [6].…”

Section: Opticalmentioning

confidence: 99%

Review of Tool Condition Monitoring Methods

Visariya¹,

Ruparel²,

Yadav³

2018

IJCA

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With the increase of modern industries use of metal cutting procedure, it is evident that the tools used for this processes required proper care and monitoring. Tool wear one of the most important factors in machining processes as it greatly affects the tool life, which is important in metal cutting because of its direct impact on the quality of the finished job and also affect the efficiency of industries. Hence, ways to observe cutting tool and monitor its wear are needed for optimal use. An effective system can reduce machine downtime and economic losses. The paper presents a overview of many Tool Condition Monitoring Systems.

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A review of machine vision sensors for tool condition monitoring

Cited by 354 publications

References 36 publications

Advanced monitoring of machining operations

Advanced monitoring of machining operations

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Review of Tool Condition Monitoring Methods

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