Study on In-process Measurement of Turning by Acoustic Emission Method

Ninomiya, Keiichi; Kumagai, Natuki; Yoshida, Shun; Koga, Toshihiko

doi:10.1299/jsmemecj.2019.s13302

Cited by 1 publication

(2 citation statements)

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“…This occurs for two reasons: a) as seen in Figure 10, transfer marks due to the flow of chips can be observed between the tool and the chips due to the progress of tool wear; and b) from the result of FEM analysis, the AE source changes from the main shear zone to the secondary plasticity zone. Since friction and wear extend to the next plastic region and the plastic region below the flank, the influence of chip friction and wear on the rake face predominantly changes in this frequency band [11]. This is consistent with the findings reported by Takuma et al [31] that the frequency range and wear conditions corresponding to the wear phenomenon can be grasped by wavelet analysis.…”

Section: Table 3: Ae Waveform and Cutting Image For Each Wear Areasupporting

confidence: 90%

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Development and Testing of a Turning Process Monitoring System using Acoustic Emission

Ninomiya¹,

Yoshida²,

Okita³

et al. 2021

JMechE

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Studies on in-process measurement have suggested techniques for sensing tool wear and machine status in a machining center and turning center, for example by measuring cutting resistance. However, there appear to be no reports of effective uses of machine sensing in practical machine tools, perhaps due to problems such as limitations on the number and density of sensors and the possibility that attaching sensors may affect machine rigidity and thus processing quality. The purpose of this research is to develop a system that can monitor the state of the cutting process and maintain it in a normal or optimal state at all times, based on the acoustic emission (AE) method. To realize the development of such a system, it is very important to qualitatively and quantitatively evaluate the tool wear. This report describes a tool wear experiment that collected AE signals, cutting force data, and high-speed camera images during metal turning. The collected basic data are comprehensively evaluated and examined to determine the effectiveness of in-process measurement by the AE method. The results suggest that evaluating various AE parameters obtained from the AE original waveform is an effective parameter for monitoring tool wear and cutting conditions that affect product quality. The findings from the basic data obtained in this study were found to be useful information for the practical application of in-process measurement by the AE method using machine learning methods.

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Section: Table 3: Ae Waveform and Cutting Image For Each Wear Areasupporting

confidence: 90%

“…Continued basic research is needed to advance the development of the Industrial Internet of Things (IIoT). To that end, a prior study constructed and tested an in-process measurement system to monitor the cutting state [11]. The acoustic emission (AE) method, a non-destructive inspection technique, was used for sensing in this system.…”

Section: Introductionmentioning

confidence: 99%