Review of Sensor Applications in Tool Condition Monitoring in Machining

Bhuiyan, M.S.H.; Choudhury, Imtiaz Ahmed

doi:10.1016/b978-0-08-096532-1.01330-3

Cited by 42 publications

(18 citation statements)

References 147 publications

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“…There is no intent to review all of them. As mentioned above, other studies on tool condition monitoring can be found in scientific databases and reviews' paper as in Bhuiyan & Choudhury (2014), Dimla Snr. (2000), Li (2002) and Rehorn et al (2005).…”

Section: Related Work On Grinding Wheel Condition Monitoringmentioning

confidence: 98%

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Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Nakai

Aguiar

Guillardi

et al. 2015

Expert Systems with Applications

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Section: Related Work On Grinding Wheel Condition Monitoringmentioning

confidence: 98%

“…The reader interested in such review can check the works of Bhuiyan and Choudhury (2014), Dimla Snr. (2000), Li (2002) and Rehorn, Jiang, and Orban (2005).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Nakai

Aguiar

Guillardi

et al. 2015

Expert Systems with Applications

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“…This is mainly due to the absence of a standalone-sensor and a highly informative signal processing technique capable of indicating the tool condition under the high dynamics of complex machining processes. Recent laboratory attempts improved their performance by using multi-sensors and/or multisignal processing techniques to enrich the system certainty [16]. However, for online applications, this may increase the system response time and lead to unnecessary alarms if the signals are not fused.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machining: Real-Time Tool Condition Monitoring and Intelligent Adaptive Control Systems

Hassan

Sadek

Attia

et al. 2018

Journal of Machine Engineering

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Unmanned manufacturing systems has recently gained great interest due to the ever increasing requirements of optimized machining for the realization of the fourth industrial revolution in manufacturing ‘Industry 4.0’. Real-time tool condition monitoring (TCM) and adaptive control (AC) machining system are essential technologies to achieve the required industrial competitive advantage, in terms of reducing cost, increasing productivity, improving quality, and preventing damage to the machined part. New AC systems aim at controlling the process parameters, based on estimating the effects of the sensed real-time machining load on the tool and part integrity. Such an aspect cannot be directly monitored during the machining operation in an industrial environment, which necessitates developing new intelligent model-based process controllers. The new generations of TCM systems target accurate detection of systematic tool wear growth, as well as the prediction of sudden tool failure before damage to the part takes place. This requires applying advanced signal processing techniques to multi-sensor feedback signals, in addition to using ultra-high speed controllers to facilitate robust online decision making within the very short time span (in the order of 10 ms) for high speed machining processes. The development of new generations of Intelligent AC and TCM systems involves developing robust and swift communication of such systems with the CNC machine controller. However, further research is needed to develop the industrial internet of things (IIOT) readiness of such systems, which provides a tremendous potential for increased process reliability, efficiency and sustainability.

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“…The state of a cutting tool is an imperative factor in any metal cutting process. The use of worn cutting tools leads directly to reduced process capabilities and product quality [1,2]. Tool condition monitoring (TCM) can be defined as a process of watching the performance of a cutting tool during machining to observe the damages occurred to it by the responsible process mechanics [2].…”

Section: Introductionmentioning

confidence: 99%

A method based on spindle motor current harmonic distortion measurements for tool wear monitoring

Akbari

Danesh

Khalili

2017

J Braz. Soc. Mech. Sci. Eng.

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Review of Sensor Applications in Tool Condition Monitoring in Machining

Cited by 42 publications

References 147 publications

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Intelligent Machining: Real-Time Tool Condition Monitoring and Intelligent Adaptive Control Systems

A method based on spindle motor current harmonic distortion measurements for tool wear monitoring

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