本文選題：數(shù)控機床 + 運動誤差��； 參考：《重慶理工大學》2015年碩士論文

【摘要】：運動誤差,作為數(shù)控機床誤差的最終反應,包含了數(shù)控機床的幾何誤差和控制誤差的信息,對數(shù)控機床的加工精度有著重大的影響;如何快速準確的監(jiān)控生產(chǎn)線上運行的數(shù)控機床的運動誤差,對抑制加工產(chǎn)品批量事故的發(fā)生及提高企業(yè)的生產(chǎn)效率具有積極的意義。而當前學者們對于數(shù)控機床精度的研究偏重于檢測、控制與補償?shù)阮I域,對誤差溯源領域的的研究卻較少。已有對誤差溯源的研究多采用誤差建模,方法復雜且依賴數(shù)控機床的結(jié)構(gòu)和類型,適用范圍較小。本文提出利用數(shù)控機床圓運動軌跡的圖形,采用圖形識別相關(guān)技術(shù),定義一種新的特征角點并開發(fā)出角點檢測算子,檢測此角點在圓運動軌跡圖形上的分布規(guī)律;將圓周分割為16維,分析各維上可反映該維整體特征的平均半徑和反映局部特征的角點個數(shù),從而建立可反映圖形特征的三維特征矩陣,并采用支持向量機對特征矩陣到誤差圖形的映射的魯棒性做了驗證,最后結(jié)合徑向基函數(shù)神經(jīng)網(wǎng)絡實現(xiàn)運動誤差源溯因網(wǎng)絡的構(gòu)建,實驗結(jié)果顯示該方法識別準確率高,識別速度快,簡便而高效。更開發(fā)了軟件系統(tǒng),簡潔明了的界面,具有優(yōu)良的友好性。采用本文所述方法尤其適合機床使用企業(yè)在制造過程中的精度溯因與控制,方法經(jīng)濟簡便,具有較大的實際應用價值。本文的主要研究內(nèi)容分為圖形的角點檢測、圖形特征提取、特征矩陣到誤差圖形映射魯棒性的驗證和綜合誤差溯源網(wǎng)絡的建立三個部分:首先是圖形的識別工作,本文提出一種新的特征角點,采用角點檢測的方法,將采集來的圓運動誤差軌跡圖形經(jīng)過預處理之后再分割為16維,經(jīng)過文中設計開發(fā)的角點檢測器檢測出符合定義的特征角點。其次,研究特征角點在分割為16維的誤差軌跡圖形的分布規(guī)律,計算各維圓周上所有點的平均半徑和特征角點個數(shù),構(gòu)建了一個三維特征矩陣,從而建立了誤差圖形與特征矩陣的映射關(guān)系。并采用支持向量機對該映射關(guān)系進行了驗證,結(jié)果顯示支持向量機對實驗樣本的分類效果顯著,表明文中所建立的映射關(guān)系魯棒性強。最后,建立基于徑向基函數(shù)神經(jīng)網(wǎng)絡的綜合誤差溯因網(wǎng)絡,以特征矩陣為輸入,各單項誤差源為網(wǎng)絡輸出。經(jīng)過訓練的溯因網(wǎng)絡最終實現(xiàn)了對綜合誤差的快速溯因,且識別率較高。
[Abstract]:Motion error, as the final reaction of NC machine tool error, contains the information of geometric error and control error of NC machine tool, which has a great influence on the machining accuracy of NC machine tool. How to quickly and accurately monitor the movement error of NC machine tools running on the production line is of positive significance to restrain the occurrence of batch accidents and to improve the production efficiency of enterprises. At present, the research on the accuracy of NC machine tools is focused on the fields of detection, control and compensation, but the research on error traceability is less. Error modeling is often used in the research of error traceability. The method is complex and depends on the structure and type of NC machine tools, and the scope of application is relatively small. In this paper, a new characteristic corner is defined and a corner detection operator is developed to detect the distribution of the corner on the circular motion trajectory by using the graph of the circular motion trajectory of the NC machine tool and the correlation technology of the graph recognition. The circle is divided into 16 dimensions, and the mean radius of the whole feature and the number of corner points reflecting the local feature on each dimension are analyzed, and the 3D feature matrix which can reflect the feature of the graph is established. The robustness of the mapping from feature matrix to error graph is verified by support vector machine. Finally, the radial basis function neural network is used to construct the moving error source tracing network. The experimental results show that the method has high recognition accuracy. The recognition speed is fast, simple and efficient. More developed software system, simple and clear interface, with good friendliness. The method described in this paper is especially suitable for the precision tracing and control in the manufacturing process of the machine tool enterprises. The method is economical and simple and has great practical application value. The main content of this paper is divided into three parts: corner detection, feature extraction, robustness verification from feature matrix to error graph mapping and establishment of comprehensive error traceability network. In this paper, a new feature corner is proposed. By using corner detection method, the collected circular motion error trajectory is preprocessed and then divided into 16 dimensions. The corner detector designed in this paper detects the characteristic corner in accordance with the definition. Secondly, the distribution law of the characteristic corner in the 16 dimensional error trajectory pattern is studied, and the mean radius and the number of characteristic corner points of all points on each dimensional circle are calculated, and a three-dimensional feature matrix is constructed. The mapping relationship between the error graph and the feature matrix is established. Support vector machine (SVM) is used to verify the mapping relationship. The results show that the classification effect of SVM on experimental samples is significant and the mapping relationship established in this paper is robust. Finally, a comprehensive error traceability network based on radial basis function neural network is established. The characteristic matrix is used as input and the single error source is network output. The trained backtracking network finally realizes the fast traceability of the synthetic error, and the recognition rate is high.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41;TG659

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