Detection of Abnormal Cutting Using Pattern Recognition of Spectrum Map. 1st Report. Detection of Self-Excited Chatter Vibration.

英二, 近藤

doi:10.1299/kikaic.63.592

Cited by 1 publication

(1 citation statement)

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“…The typical monitoring technique operates according to the vibrations and acoustic emission because these are influenced by the cutting tool state and the material removal process conditions [1,2]. However, most of the proposing methods have low detection sensitivity without a variety of statistical processing of sensor data [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Utilization Method of Big Sensor Data to Detect Tool Anomaly in Machining Process

Kondo

Mizunoya

Sakamoto

et al. 2016

KEM

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The essential features and scale of sensor data was discussed to monitor the tool anomaly in the machining process from the pattern variation of large scale sensor data such as vibration and effective power. The cycle data, the time series sensor data collected with an acceleration or power sensor in one periodical machining of the given groove shape, had been measured periodically. In this study, the graphic pattern formed by overwriting the time series cycle data on a specific coordinate system was treated as the “big sensor data”. The big data from the effective power sensor can stably respond to the cutting power changes and showed a strong possibility as a detecting device for tool anomaly such as abrasive wear and chipping. While the big data from the acceleration sensor only responded to a big event like the chattering vibration. The number of cycle data needed to generate the big sensor data also affected on the detection sensitivity for tool anomaly. It had been required a family of time series sensor data enough to represent the cutting power change as a visual graphic pattern.

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