發(fā)布時間：2018-04-03 13:44

  本文選題：銑削刀具　切入點：刀具磨損　出處：《哈爾濱理工大學》2016年碩士論文

【摘要】：隨著工業(yè)智能化的提出,數(shù)控加工技術正朝著智能化的方向發(fā)展。刀具磨損狀態(tài)監(jiān)測技術在數(shù)控加工智能化發(fā)展中占據(jù)重要的地位,在加工過程中,精確及時地監(jiān)測到刀具磨損狀態(tài)并且更換失效的刀具,不僅有助于提高生產效率,還可以提高刀具的使用壽命。因此,刀具磨損狀態(tài)監(jiān)測系統(tǒng)是現(xiàn)代數(shù)控加工智能化發(fā)展迫切需要的,其關鍵技術的研究具有十分重要的意義。本文針對刀具磨損的形成機理進行了詳盡的分析,根據(jù)分析制定刀具磨鈍標準,考慮不同磨損狀態(tài)下對監(jiān)測信號的影響,合理選擇監(jiān)測方法,建立銑刀磨損狀態(tài)監(jiān)測系統(tǒng)的信號采集平臺,制定信號采集方案并且采集信號數(shù)據(jù)。針對上述所采集的數(shù)據(jù)進行特征提取,建立特征向量與銑削刀具磨損的之間的關系,針對特征向量維數(shù)高的特點,提出了改進的多分類支持向量機遞歸特征消去的特征選擇方法,運用測試樣本進行驗證,該方法優(yōu)于其他特征選擇方法,為后續(xù)的狀態(tài)識別,提供維數(shù)低和對不同銑刀磨損狀態(tài)比較敏感的特征向量。采用萬有引力優(yōu)化最小二乘支持向量機的銑刀磨損狀態(tài)識別的方法,利用選擇后的特征向量進行銑刀磨損狀態(tài)的識別。通過對比,該方法的識別精度較高。本文最后結合VC++軟件和Matlab軟件的優(yōu)點,開發(fā)銑削刀具磨損狀態(tài)監(jiān)測系統(tǒng),可以實現(xiàn)銑削加工中刀具磨損狀態(tài)的快速識別,通過識別結果顯示界面,用戶可以直觀地判斷刀具磨損的狀況,進而判斷是否更換刀具,減少停機時間,提高了加工效率。通過對銑削刀具磨損狀態(tài)監(jiān)測系統(tǒng)關鍵技術的研究,提高了刀具的利用率,避免了報廢工件的產生,降低了生產過程中資源的大量浪費,提高了生產效率。為數(shù)控加工智能化的發(fā)展奠定了基礎。
[Abstract]:With the advance of industrial intelligence, NC machining technology is developing towards the direction of intelligentization.Tool wear monitoring technology plays an important role in the intelligent development of NC machining. In the process of machining, it is not only helpful to improve the production efficiency, but also to monitor the tool wear state accurately and timely and to replace the invalid tool in the process of machining.It can also improve the service life of cutting tools.Therefore, tool wear monitoring system is an urgent need for the intelligent development of modern NC machining, and the research of its key technology is of great significance.In this paper, the formation mechanism of tool wear is analyzed in detail. According to the analysis, the tool wear bluntness standard is formulated, the influence of different wear states on monitoring signal is considered, and the monitoring method is reasonably selected.The signal acquisition platform of milling cutter wear monitoring system is established, and the signal acquisition scheme and signal data collection are made.Based on the above data, the relationship between feature vector and milling tool wear is established, and the feature vector dimension is high.An improved feature selection method based on recurrent feature elimination of multi-classification support vector machines is proposed, which is verified by test samples. This method is superior to other feature selection methods and is used for subsequent state recognition.Provides feature vectors with low dimension and sensitivity to different milling cutter wear states.The wear state of milling cutter is identified by using the universal gravity optimization least squares support vector machine (LS-SVM) and the selected eigenvector.By comparison, the recognition accuracy of this method is high.Finally, combining the advantages of VC software and Matlab software, a tool wear monitoring system is developed, which can realize the rapid recognition of tool wear state in milling machining, and display the interface through the recognition results.The user can directly judge the condition of tool wear, and then judge whether to change the tool, reduce the downtime and improve the machining efficiency.Through the research on the key technology of the monitoring system of milling tool wear status, the utilization rate of the tool is improved, the production of scrapped workpiece is avoided, the waste of resources in the production process is reduced, and the production efficiency is improved.It lays a foundation for the development of intelligent NC machining.
【學位授予單位】：哈爾濱理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TG54;TG714

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