本文關(guān)鍵詞： 小波分析 健康監(jiān)控 高速加工 神經(jīng)網(wǎng)絡　出處：《河北工業(yè)大學》2011年碩士論文　論文類型：學位論文

【摘要】：隨著高速加工技術(shù)的發(fā)展,制造過程中的工況監(jiān)控所遇到的問題也越來越突出,且直接影響了生產(chǎn)效率,因此對高速CNC集成制造過程進行監(jiān)控,才能保證產(chǎn)品質(zhì)量,提高加工效率,延長刀具使用壽命,確保人員和設備的安全。把結(jié)構(gòu)健康監(jiān)控的概念引入到高速CNC集成制造系統(tǒng)中,可用一些工況信息(如切削力、扭矩、功率、主軸變速、振動、熱變形等)來描述數(shù)控加工過程中的機床的各種狀態(tài)。高速CNC集成制造健康監(jiān)控涉及到主軸發(fā)熱監(jiān)測、滾珠絲杠發(fā)熱監(jiān)測、刀具磨損狀態(tài)監(jiān)測、工件加工狀態(tài)監(jiān)測等。本文著重研究刀具的工況健康監(jiān)控方法,提出的方法也使用與其他方面。 本文利用振動傳感器對刀具磨損信號進行采集,并對所采集的信號進行分析處理,通過對比不同磨損程度的刀具信號,提取與刀具磨損相關(guān)的特征值。時域方面,提取出信號的均方根作為特征值;時頻域方面,采用了小波包分解頻帶能量監(jiān)測法,對振動信號進行了頻段能量統(tǒng)計,提取出信號的特征頻段能量作為特征值。再此基礎上建立基于BP神經(jīng)網(wǎng)絡和D-S證據(jù)理論相結(jié)合的車刀故障綜合診斷模型,豐富和發(fā)展了刀具磨損監(jiān)控技術(shù)。最后在上述研究的基礎上,構(gòu)建了VC與MATLAB混合編程開發(fā)系統(tǒng)數(shù)據(jù)處理平臺
[Abstract]:With the development of high speed machining technology, the problems of working condition monitoring in manufacturing process are more and more prominent, and directly affect the production efficiency. Therefore, monitoring the high speed CNC integrated manufacturing process can ensure the product quality. The concept of structural health monitoring is introduced into the high speed CNC integrated manufacturing system, and some working conditions information (such as cutting force, torque, power, spindle speed change, etc.) can be used to improve the machining efficiency, prolong the tool life and ensure the safety of the personnel and equipment. High speed CNC integrated manufacturing health monitoring involves spindle heating monitoring, ball screw heating monitoring, tool wear monitoring, This paper focuses on the research of the tool condition health monitoring method, the proposed method is also used and other aspects. In this paper, the tool wear signal is collected by vibration sensor, and the collected signal is analyzed and processed. By comparing the tool signal with different wear degree, the characteristic value related to tool wear is extracted. The root mean square (RMS) of the signal is extracted as the eigenvalue, and in time-frequency domain, the frequency band energy monitoring method based on wavelet packet decomposition is used to calculate the frequency band energy of the vibration signal. The characteristic frequency band energy of the signal is extracted as the eigenvalue, and then a comprehensive fault diagnosis model of turning tool based on BP neural network and D-S evidence theory is established. The tool wear monitoring technology is enriched and developed. Finally, the data processing platform of VC and MATLAB hybrid programming system is constructed based on the above research.
【學位授予單位】：河北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH166

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