本文選題：高速銑削加工 + 刀具磨損狀態(tài)監(jiān)測(cè)�。� 參考：《中國科學(xué)技術(shù)大學(xué)》2017年碩士論文

【摘要】：近年來高速銑削加工技術(shù)憑借其出眾的加工精度、加工效率和表面質(zhì)量等優(yōu)勢(shì)在眾多領(lǐng)域得到了廣泛應(yīng)用。在高速銑削加工中,銑刀在超高轉(zhuǎn)速下進(jìn)行不連續(xù)切削,刀具磨破損迅速,直接影響加工精度與產(chǎn)品質(zhì)量,嚴(yán)重時(shí)甚至損壞機(jī)床和工件,引起事故。因此,對(duì)高速銑削加工過程中刀具的磨損狀況進(jìn)行實(shí)時(shí)的在線監(jiān)測(cè)意義重大。本文借助于先進(jìn)的傳感技術(shù),在稀疏表示和模式識(shí)別的基礎(chǔ)上提出了一種新的故障診斷方法,以達(dá)到對(duì)刀具磨損狀態(tài)的實(shí)時(shí)監(jiān)測(cè),提高生產(chǎn)系統(tǒng)的安全性。論文的主體工作包含以下幾點(diǎn):(1)學(xué)習(xí)并總結(jié)了近年來在高速銑削加工領(lǐng)域針對(duì)于刀具磨破損狀態(tài)監(jiān)測(cè)的各種科學(xué)方法,并介紹了各個(gè)科研機(jī)構(gòu)和學(xué)者的研究進(jìn)展,研究了刀具磨損的機(jī)理和分類問題,為該課題的開展奠定扎實(shí)的理論基礎(chǔ)。(2)基于壓縮感知和稀疏表示的理論,結(jié)合形態(tài)分量分析和增廣拉格朗日變量分離算法,構(gòu)造對(duì)偶BP優(yōu)化模型,并使用SALSA算法對(duì)優(yōu)化模型進(jìn)行求解,達(dá)到了對(duì)信號(hào)的脈沖成分和諧波成分進(jìn)行分離的目的.隨后對(duì)該算法的分離效果進(jìn)行仿真分析和驗(yàn)證。(3)搭建高速銑削加工實(shí)驗(yàn)平臺(tái),介紹了該平臺(tái)的理論構(gòu)成和各模塊的使用情況,并進(jìn)行了傳感信號(hào)的采集與儲(chǔ)存。針對(duì)加工過程中振動(dòng)信號(hào)的特點(diǎn)及其在頻域上的稀疏特性,對(duì)振動(dòng)信號(hào)進(jìn)行稀疏分解和形態(tài)分量分析,分離出振動(dòng)信號(hào)中的脈沖成分和諧波成分。對(duì)分離后的信號(hào)分量分別提取脈沖密度和高次諧波頻率與基頻的幅值比等特征,并分別與刀具磨損形成的向量之間的相關(guān)性,探索這些特征的物理意義和在刀具磨損狀態(tài)監(jiān)測(cè)上的實(shí)用性。(4)構(gòu)造多類別支持向量機(jī)分類器,將通過稀疏分解得到的特征樣本輸入分類器中進(jìn)行訓(xùn)練和學(xué)習(xí),使該分類器具備通過多個(gè)特征有效地辨識(shí)刀具所處的磨損狀態(tài)的功能,并將新的實(shí)驗(yàn)數(shù)據(jù)輸入到分類器中進(jìn)行刀具磨損狀態(tài)預(yù)測(cè),以進(jìn)行驗(yàn)證該分類器分類效果。
[Abstract]:In recent years, high speed milling technology has been widely used in many fields because of its outstanding machining accuracy, processing efficiency and surface quality. In high speed milling, the milling cutter is discontinuous cutting at ultra-high speed, and the tool wear is damaged quickly, which directly affects the machining precision and product quality, and even damages the machine tool and workpiece seriously, causing accidents. Therefore, it is of great significance to monitor tool wear in real time during high speed milling. In this paper, a new fault diagnosis method based on sparse representation and pattern recognition is proposed with the help of advanced sensing technology to achieve real-time monitoring of tool wear and improve the safety of production system. The main work of this paper includes the following points: 1) Learning and summing up various scientific methods in the field of high speed milling in recent years, and introducing the research progress of various scientific research institutions and scholars. This paper studies the mechanism and classification of tool wear, which lays a solid theoretical foundation for the development of the subject. It is based on the theory of compressed perception and sparse representation, combined with morphological component analysis and augmented Lagrangian variable separation algorithm. The dual BP optimization model is constructed and the SALSA algorithm is used to solve the optimization model. The aim of separating the pulse component from the harmonic component of the signal is achieved. Then the separation effect of the algorithm is simulated and verified. (3) A high-speed milling experimental platform is built. The theoretical structure of the platform and the usage of each module are introduced, and the sensing signals are collected and stored. According to the characteristics of vibration signal and its sparsity in frequency domain, the vibration signal is decomposed by sparse decomposition and morphological component analysis, and the pulse component and harmonic component of vibration signal are separated. For the separated signal components, the characteristics such as pulse density, amplitude ratio of high harmonic frequency to fundamental frequency are extracted, respectively, and the correlation between them and the vectors formed by tool wear is obtained, respectively. To explore the physical meaning of these features and the practicability of tool wear state monitoring, a multi-class support vector machine classifier is constructed, and the feature samples obtained by sparse decomposition are trained and studied in the classifier. The classifier has the function of identifying the wear state of the tool effectively through several features, and the new experimental data are input into the classifier to predict the tool wear state, so as to verify the classification effect of the classifier.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG54

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