本文關(guān)鍵詞： 單齒BTA鉆 刀具磨損 小波分析 神經(jīng)網(wǎng)絡(luò)　出處：《中北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：深孔加工在機(jī)械制造業(yè)中占據(jù)有重要地位,深孔刀具則是深孔加工的核心部件。BTA深孔鉆作為深孔加工的常用刀具,最容易出現(xiàn)的問題是刀具磨損,直接影響著加工質(zhì)量。因此對深孔刀具磨損情況進(jìn)行研究并能夠及時監(jiān)測到鉆頭的磨損狀態(tài),進(jìn)行刀具磨損狀態(tài)的識別具有深遠(yuǎn)的意義。論文介紹了單齒BTA內(nèi)排屑深孔鉆的結(jié)構(gòu),將刀具磨損狀態(tài)分為正常磨損、過度磨損和崩刃三類,并分析了其磨損形式、影響磨損的因素和磨損機(jī)理。使用Deform-3D有限元軟件對深孔加工過程進(jìn)行模擬仿真,并根據(jù)運(yùn)行得到的結(jié)果分析了加工過程中的溫度場分布和刀具磨損情況。論文以BTA內(nèi)排屑深孔鉆削系統(tǒng)為研究對象,在對深孔加工特點(diǎn)以及工況信號進(jìn)行分析的基礎(chǔ)上,建立了以切削功率為監(jiān)測信號的深孔刀具狀態(tài)監(jiān)測系統(tǒng),并對采集到的功率信號進(jìn)行了消噪處理和特征提取,最后對特征值進(jìn)行識別。由于加工環(huán)境的復(fù)雜性,直接采集的功率信號含有非平穩(wěn)特性以及包含干擾噪聲,必須首先進(jìn)行處理,因此進(jìn)行了消噪過程。然后使用小波分析的方法對消噪后的功率信號進(jìn)行特征提取。利用小波變換原理對功率信號進(jìn)行多層分解與重構(gòu),提取出與刀具磨損狀態(tài)有較強(qiáng)相關(guān)性的高頻頻帶能量,將其作為特征向量。最后建立基于RBF神經(jīng)網(wǎng)絡(luò)的深孔加工刀具磨損識別模型,實現(xiàn)特征向量向刀具磨損狀態(tài)的映射。結(jié)果表明,該模型對深孔鉆削中刀具磨損狀態(tài)能夠較好的進(jìn)行識別。
[Abstract]:Deep hole machining occupies an important position in the mechanical manufacturing industry. Deep hole tool is the core part of deep hole machining. BTA deep hole drill is the common tool for deep hole machining. The most common problem is tool wear. It has a direct impact on the machining quality. Therefore, the wear of the deep hole tool can be studied and the wear state of the bit can be monitored in time. It is of great significance to recognize the tool wear state. The structure of single-tooth BTA deep hole drill is introduced in this paper. The tool wear state is divided into normal wear, excessive wear and breakage. The wear form, the factors affecting the wear and the wear mechanism are analyzed. The deep hole machining process is simulated by using Deform-3D finite element software. According to the results obtained from the operation, the temperature field distribution and tool wear in the machining process are analyzed. The paper takes the BTA deep hole drilling system as the research object. On the basis of analyzing the characteristics of deep hole machining and the signal of working condition, a condition monitoring system of deep hole cutting tool is established, which takes cutting power as the monitoring signal. The acquired power signal is de-noised and feature extracted. Finally, the characteristic value is identified. Because of the complexity of the processing environment, the directly collected power signal contains non-stationary characteristics and interference noise. It must be processed first, so the process of de-noising is carried out. Then wavelet analysis is used to extract the features of the de-noised power signal, and the wavelet transform principle is used to decompose and reconstruct the power signal. The high frequency band energy which has strong correlation with the tool wear state is extracted and used as the eigenvector. Finally, the recognition model of tool wear for deep hole machining based on RBF neural network is established. The mapping of feature vector to tool wear state is realized. The results show that the model can recognize the tool wear state in deep hole drilling.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG713

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