Detection of Ductile to Brittle Transition in Microindentation and Microscratching of Single Crystal Silicon Using Acoustic Emission

Koshimizu, Shigeomi; Otsuka, Jiro

doi:10.1081/mst-100103180

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…14) during cutting are already known as major sources of AE signal generations [29]. With the depth near or over d c value (brittle regime), not just AE energy levels, but more sophisticated analysis such as detailed spectral analysis should be performed to investigate various signal characteristics during the deformation and/or fracture processes [19][20]30].…”

Section: Ae Monitoring During Nano-scratchingmentioning

confidence: 99%

“…These low intensity, high frequency (100 kHz-1 MHz) elastic waves propagate in all directions through the structure to a detector on the surface of the workpiece. In previous research, AE has been shown to be sensitive to a variety of characteristics in precision manufacturing processes including nano-order applications [12][13][19][20]]. Fig.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of ductile mode and brittle transition of AFM nanomachining of silicon

Lee

2012

International Journal of Machine Tools and Manufacture

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Section: Ae Monitoring During Nano-scratchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of ductile mode and brittle transition of AFM nanomachining of silicon

Lee

2012

International Journal of Machine Tools and Manufacture

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“…In [32,33] the registration of acoustic emission was proposed as a means of determining a ductile brittle transition in scratching silicon [32], silicon carbide and quartz [33]. A method of the direct infrared optical registration of phase transitions in scratching is described in [34].…”

Section: Plastic Brittle Transition and Phase Transformationsmentioning

confidence: 99%

Studies of the ductile mode of cutting brittle materials (A review)

Kovalchenko

2013

J. Superhard Mater.

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Theoretical and experimental studies of the ductile mode of cutting brittle materials (semicon ductors, ceramics, and glass) have been considered. The ductile mode of cutting has been based on the implementation of high pressure induced phase transformations in a material machined that followed by a cutting of a transformed amorphous layer, which makes it possible to avoid cracking. Publications on studies of phase transitions in brittle materials in the course of the indentation, scratching, friction, and cutting have been reviewed. It has been shown that the cutting depth, cutting edge radius of a tool, chip thickness, tool cutting edge inclination, and crystallographic orientation of a material machined and dia mond tool as well as a type of lubricoolant are the decisive factors in implementing the ductile mode of cutting. KOVALCHENKO 1 2 Cutting length 3 4 Resistance force, N Fig. 3. Scheme of different modes of removing a brittle material with increasing normal load and cutting depth [42]: elastic (1) and elastoplastic (2) deformations, plastic (3) and brittle (4) removal of the material.

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“…These lowintensity high-frequency (100 kHz to 1 MHz) elastic waves propagate in all directions through the structure to a detector on the surface of the workpiece. Previous research has shown that AE is sensitive to a variety of characteristics in precision manufacturing processes, including ultra-precision nano-order applications [19][20][21][22]. The generation of AE signals is subject to the characteristics of object materials used in the polishing process, and relevant information can be extracted from AE signals using various signal processing techniques, including time series and spectral analysis according to process features.…”

Section: Ae and Force Signals During Mafmentioning

confidence: 99%

Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion

Lee

2011

Proceedings of the Institution of Mechanical Engineers, Part B:

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The configuration of automated polishing systems requires the implementation of monitoring schemes to estimate surface roughness. In this study, a precision polishing process – magnetic abrasive finishing (MAF) – was investigated together with an in-process monitoring set-up. A specially designed magnetic quill was connected to a CNC machining center to polish the surface of Stavax (S136) die steel workpieces. During finishing experiments, both acoustic emission (AE) signals and force signals were sampled and analyzed. The finishing results show that MAF has nanoscale finishing capability (up to 8nm in surface roughness), and the sensor signals have strong correlations with parameters such as the gap between the tool and workpiece, feed rate, and abrasive size. In addition, the signals were utilized as input parameters of artificial neural networks (ANNs) to predict generated surface roughness. To increase accuracy and resolve ambiguities in decision making/prediction from the vast amount of data generated, sensor data fusion (AE + force)-based ANN and sensor information-based ANN were constructed. Among the three types of networks, the ANN constructed using sensor fusion produced the most stable outcomes. The results of this analysis demonstrate that the proposed sensor (fusion) scheme is appropriate for monitoring and prediction of nanoscale precision finishing processes.

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Detection of Ductile to Brittle Transition in Microindentation and Microscratching of Single Crystal Silicon Using Acoustic Emission

Cited by 19 publications

References 10 publications

Analysis of ductile mode and brittle transition of AFM nanomachining of silicon

Analysis of ductile mode and brittle transition of AFM nanomachining of silicon

Studies of the ductile mode of cutting brittle materials (A review)

Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion

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