Surface quality and burr characterization during drilling CFRP/Al stacks with acoustic emission monitoring

Zhang, Xiaodong; Li, Maojun; Huang, Dingxiao

doi:10.1016/j.jmapro.2023.04.076

Cited by 14 publications

(1 citation statement)

References 32 publications

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“…Numerous studies have been conducted in which AE sensing was used to monitor conditions during various machining operations, such as turning [8][9][10][11], milling [12][13][14][15], reaming [16], honing [17,18], grinding [19][20][21][22][23], or polishing [24][25][26]. Several AE sensing studies have also been reported for drilling [27][28][29]; for example, Gómez and Ferrari described correlations between various AE parameters and thrust force and tool wear [30,31], whereas Patra reported the usefulness of an artificial-neural-network model based on wavelet-packet features for evaluating flank wear [32]. Furthermore, for the evaluation of flank wear of small-diameter drill bits, deep-feature-distribution modelling (a method for image-level anomaly detection and anomaly segmentation in time-series signal analysis) has been proposed by Nakano et al [33].…”

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confidence: 99%

In Situ Measurement of the Machining State in Small-Diameter Drilling by Acoustic Emission Sensing

Hase

2024

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In drilling small holes with diameters of 1 mm or less, minute clogging and twining of chips or the adhesion of the workpiece material can become factors in causing breakages of the drill bit; moreover, it can be difficult to identify the machining state. Acoustic emission (AE) sensing is a nondestructive inspection technique that measures the elastic-stress waves that are generated when a material is deformed and fractured. AE sensing permits highly sensitive measurements to be made without changing the rigidity of the experimental system, unlike force sensing of cutting resistance, etc. In the present study, attempts were made to identify the machining state and tool wear, and to predict abnormalities in small-diameter drilling by using the change in the frequency of AE signal waveforms arising from deformation and fracture. It was shown that it is possible to predict the breakage of the drill bit by detecting high-frequency AE signals at about 1 MHz, caused by the adhesion of the workpiece material. In addition, a correlation map of the AE frequency spectrum for identifying the machining state in a drilling operation is suggested.

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