Acoustic Emission Monitoring of Tool Wear in End-Milling Using Time-Domain Averaging

Hutton, David V.; Hu, Fangming

doi:10.1115/1.2830581

Cited by 28 publications

(10 citation statements)

References 8 publications

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“…By the location of the AE sensor on the coolant supply nozzle, the coolant can be used as transmission path [46]. Hutton and Hu [47] used a nonintrusive coupling fluid to couple the AE sensor to the spindle drive shaft, similar to Li et al [48]. These signal transmission methods had a distinct advantage for rotating tools such as in milling and drilling.…”

Section: Ae Signal Transmission and Sensor Locationmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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Section: Ae Signal Transmission and Sensor Locationmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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“…The simplest algorithmic method uses the root mean square value (RMS) of the captured data using AE sensors [31]. Advanced ones include artificial neural networks [32], use of statistical classifiers (SVM and ARD) [33], and signal processing approaches like time domain analysis [34].…”

Section: Use Of Acoustic Emission Sensorsmentioning

confidence: 99%

Applicability of Tool Condition Monitoring Methods Used for Conventional Milling in Micromilling: A Comparative Review

Mandal

2014

Journal of Industrial Engineering

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Micromilling is a contact based material removal process in which a rotating tool with nose radius in microns is fed over a stationary workpiece. In the process small amount of material gets chipped off from the workpiece. Due to continuous contact between tool and workpiece significant damage occurs to the cutting tools. Mitigating tool damage to make micromilling systems more reliable for batch production is the current research trend. In macroscale or conventional milling process a number of methods have been proposed for tool condition monitoring. Few of them have been applied for micromilling. This paper reviews different methods proposed and used in last two decades for monitoring the condition of micromilling tools. Applicability of tool condition monitoring methods used in conventional milling has been compared with the similar ones proposed for micromilling. Further, the challenges and opportunities on the applicability issues have been discussed.

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“…The monitoring task of a process is composed of three main parts: sensing of the process, signal processing and monitoring decision-making. Different measures have been studied for machine tool monitoring: cutting force (Mashine et al, 1999), acoustic emission (Rice and Wu, 1993;Diei and Dornfeld, 1987;Kamarthi et al, 2000;Hutton and Hu, 1999), spindle motor current (Altintas, 1992), machine tool vibration (Li et all, 1998) and ultrasonic energy (Colgan et al, 1994). The different works have revealed there is no a signature extracted from a single signal free of false alarms in the detection process the cutting process abnormalities (wear, breakage, chipping).…”

Section: Machine Tool Condition Monitoringmentioning

confidence: 99%