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1.
公开(公告)号:US20240173809A1
公开(公告)日:2024-05-30
申请号:US18346546
申请日:2023-07-03
Inventor: Sung Jae YOON , Munyoung LEE , Seung Hyub JEON , Jung-Chan NA
CPC classification number: B23Q5/58 , B23Q3/155 , G01M99/005 , B23Q2003/15586
Abstract: The present invention relates to verifying the suitability of a tool and diagnosing a spindle for a machining center on which different tools are mounted. Provided are a machining center spindle diagnosis apparatus and method configured to monitor a change of a tool in the machining center; when the change of the tool is recognized, control the machining center to idle the spindle; acquire sensor data from a sensor installed on the machining center during the idling of the spindle; input the acquired sensor data to a tool verifying model pre-trained by a machine learning technology to verify suitability of the tool; and when the tool is verified to be suitable, inputting the acquired sensor data to a spindle diagnosing model pre-trained by the machine learning technology to diagnose an operating state of the spindle.
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公开(公告)号:US20230314282A1
公开(公告)日:2023-10-05
申请号:US18193346
申请日:2023-03-30
Inventor: Munyoung LEE , Sung Jae YOON , Jung-Chan Na , Jeong Hwan Lee , Seung Hyub Jeon
CPC classification number: G01M99/005 , G05B13/0265
Abstract: In order to increase the accuracy of status or failure diagnosis of machine tools and reduce the time and costs required for data collection and algorithm development for diagnosis, sensors are mounted on specific elements of the machine tools and measurement data is collected while operating under the specific operating conditions during a time interval between one machining operation and another machining operation to perform status or failure diagnosis of the machine tools. According to the present invention, by using data collected while operating machine tools under the predefined specific conditions using a time interval between one machining operation and another machining operation, it is possible to more accurately perform diagnosis while allowing less noise to be mixed in the data and reducing the time required for data collection for the development of diagnostic algorithm.
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