型鋼加工中心刀具狀態評估與切削液異常監測之研究

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Title:	型鋼加工中心刀具狀態評估與切削液異常監測之研究
Authors:	黃琮仁;Huang, Tsung-Ren
Contributors:	機械工程學系
Keywords:	鋸帶;鑽頭;狀態監測;自組織映射;切削水異常監測;Band Saws;Drill;Condition Monitoring;Self-Organizing Map;cutting fluid abnormal monitoring
Date:	2018-11-27
Issue Date:	2019-04-02 15:12:06 (UTC+8)
Publisher:	國立中央大學
Abstract:	本研究以振動訊號量測帶鋸床鋸臂以及鑽孔機主軸上之切削振動情形，以及鑽孔機主軸馬達電流變化，進行型鋼加工中心刀具狀態監測；另外，也利用訊號特徵監測機器切削液的供應狀態，建立冷卻失效的異常警示。研究涵蓋頻譜分析以及自組織映射(Self-Organizing Map, SOM)演算法。本研究使用加速規及電流勾錶量測加工時的振動及電流訊號，將其進行頻譜分析，提取代表性訊號特徵，最後將特徵以自組織映射將數據訓練及測試，得知刀具從全新至老化之狀態趨勢，達到刀具健康狀態監測之目的。在切削加工製程的異常部分，將刀具在切削液不足及正常加工的特徵進行比較，建立供應失效的異常警示。本研究中是在型鋼加工生產線進行實驗數據收集，對帶鋸機鋸臂、鑽孔機主軸布置加速規，亦對馬達電流進行監測。;In this study, the vibration signal is used to measure the cutting vibration of the band saw blade arm and the spindle of the drilling machine, and the current of the spindle motor of the drilling machine in order to monitor the tool state of the beam welding line. In addition, the signal features also used to monitor the state of cutting fluid, and then established the warning of the cooling failure. The research consists of spectrum analysis and Self-Organizing Map (SOM) algorithms. In this study, the accelerometer and current hook meter were used to measure the vibration and current signals during processing, and the spectrum analysis was performed to extract the representative signal features. Finally, the features are trained and tested by self-organizing mapping. and the trend from new to aging is known to achieve the purpose of tool health monitoring. In the abnormal cutting process, the tool is compared with the features of insufficient cutting fluid and normal to establish the abnormal warning of cutting fluid failure. In this study, the experimental data was collected on the beam welding line. The acceleration is installed on the saw arm of the band saw machine and the spindle of the drilling machine, and also monitor the motor current.
Appears in Collections:	[Graduate Institute of Mechanical Engineering] Electronic Thesis & Dissertation

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