本文選題：刀具磨損 + 時(shí)域分析　； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：如今,從國(guó)際角度來看,制造業(yè)地位日益凸顯,以智能制造為代表的科技變革,正在將全球制造業(yè)推倒重建,形成新的“工業(yè)互聯(lián)網(wǎng)”世界,并成為國(guó)際競(jìng)爭(zhēng)戰(zhàn)略高地。在制造業(yè)都在向智能制造方向發(fā)展的同時(shí),數(shù)控加工技術(shù)的智能化水平也得到迅速提高。在機(jī)械加工中,大部分的零件都是由切削加工生產(chǎn)得到的,刀具的使用是最直接最頻繁的。實(shí)時(shí)準(zhǔn)確獲知刀具的磨損狀態(tài)對(duì)提高加工產(chǎn)品精度和表面質(zhì)量、實(shí)現(xiàn)個(gè)性化制造,提高機(jī)床智能化水平、提高系統(tǒng)誤差補(bǔ)償技術(shù)具有實(shí)際意義。本文的工作內(nèi)容如下:1、研究了聲發(fā)射信號(hào)的特點(diǎn),確定了以聲發(fā)射信號(hào)為監(jiān)測(cè)信號(hào)的在線監(jiān)測(cè)方案。搭建了刀具磨損試驗(yàn)系統(tǒng),通過試驗(yàn)研究了切削過程中刀具磨損形式,確定了刀具磨損的磨鈍標(biāo)準(zhǔn),將刀具磨損劃分為前期、中期、后期磨損三個(gè)階段。基于正交試驗(yàn)研究了聲發(fā)射信號(hào)隨刀具磨損、主軸轉(zhuǎn)速、進(jìn)給量和背吃刀量四個(gè)因素的變化規(guī)律。2、用不同的信號(hào)處理方法提取刀具磨損的特征值。基于時(shí)域分析方法,提取了均值、均方根、方差和方根幅值;基于小波包變換的分析方法,提取頻段的能量比作為刀具磨損的特征值;基于經(jīng)驗(yàn)?zāi)B(tài)分析方法,提取特征模態(tài)函數(shù)的均方根作為刀具磨損的特征值。對(duì)提取的時(shí)域特征值和時(shí)頻特征值進(jìn)行選擇和優(yōu)化。3、將優(yōu)化后的特征值輸入到LS-SVM算法中進(jìn)行學(xué)習(xí),建立刀具磨損狀態(tài)識(shí)別模型。LS-SVM算法中的懲罰因子c和核參數(shù)g對(duì)識(shí)別的準(zhǔn)確率有很大的影響,將粒子群算法應(yīng)用到LS-SVM算法中,在全局化與收斂速度方面具有較大優(yōu)勢(shì),能夠?qū)崿F(xiàn)參數(shù)c和g的快速尋優(yōu)。建立基于PSO-LS-SVM的刀具磨損狀態(tài)識(shí)別模型和磨損量預(yù)測(cè)模型,建立未優(yōu)化的LS-SVM和BP神經(jīng)網(wǎng)絡(luò)刀具磨損模型,用測(cè)試樣本檢測(cè)三個(gè)模型的預(yù)測(cè)效果,結(jié)果表明PSO-LS-SVM的準(zhǔn)確率最高。最后,離線檢測(cè)刀具磨損對(duì)已加工表面的殘余應(yīng)力和粗糙度,將離線檢測(cè)的結(jié)果用于間接評(píng)估刀具磨損,同時(shí)驗(yàn)證了刀具磨損的預(yù)測(cè)結(jié)果。
[Abstract]:Now, from the international point of view, the status of manufacturing industry is increasingly prominent. The technological changes represented by intelligent manufacturing are pushing the global manufacturing industry down and rebuilding, forming a new "industrial Internet" world and becoming the strategic high ground of international competition. With the development of manufacturing industry towards intelligent manufacturing, the intelligent level of NC machining technology has been improved rapidly. In machining, most parts are produced by cutting, and the use of cutting tools is the most direct and frequent. It is of practical significance to know the wear state of cutting tools in real time and accurately for improving the precision and surface quality of machining products, realizing individualized manufacturing, improving the intelligent level of machine tools and improving the compensation technology of system errors. The work of this paper is as follows: 1. The characteristics of acoustic emission signal are studied and the on-line monitoring scheme with acoustic emission signal as monitoring signal is determined. The tool wear test system was set up, and the tool wear form in the cutting process was studied, and the grinding bluntness standard was determined. The tool wear was divided into three stages: early, middle and late wear stages. Based on orthogonal test, the variation of acoustic emission signal with tool wear, spindle speed, feed rate and feed rate was studied. Different signal processing methods were used to extract the characteristic value of tool wear. Based on time domain analysis method, the mean value, root mean square, variance and square root amplitude are extracted. Based on wavelet packet transform, the energy ratio of frequency band is extracted as the characteristic value of tool wear. The root mean square (RMS) of the eigenmode function is extracted as the eigenvalue of tool wear. The extracted time-domain eigenvalues and time-frequency eigenvalues are selected and optimized. The optimized eigenvalues are input into the LS-SVM algorithm for learning. The penalty factor c and kernel parameter g of tool wear recognition model. LS-SVM algorithm have great influence on the accuracy of recognition. The particle swarm optimization algorithm is applied to LS-SVM algorithm, which has a great advantage in globalizing and convergence speed. The parameters c and g can be optimized quickly. Based on PSO-LS-SVM, the tool wear recognition model and wear quantity prediction model are established, and the unoptimized LS-SVM and BP neural network tool wear models are established. The results show that the accuracy of PSO-LS-SVM is the highest. Finally, the residual stress and roughness of tool wear on machined surface are detected offline. The results of off-line testing are used to evaluate the tool wear indirectly, and the prediction results of tool wear are verified at the same time.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG71

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 邵欽作;;機(jī)床行業(yè)“智能制造”的發(fā)展與展望[J];世界制造技術(shù)與裝備市場(chǎng);2016年06期

2 張存吉;姚錫凡;張劍銘;劉二輝;;基于深度學(xué)習(xí)的刀具磨損監(jiān)測(cè)方法[J];計(jì)算機(jī)集成制造系統(tǒng);2017年10期

3 賀正楚;潘紅玉;;德國(guó)“工業(yè)4.0”與“中國(guó)制造2025”[J];長(zhǎng)沙理工大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2015年03期

4 邱英;謝鋒云;;基于小波包系數(shù)與隱馬爾科夫模型的刀具磨損監(jiān)測(cè)(英文)[J];機(jī)床與液壓;2014年12期

5 費(fèi)強(qiáng);趙武云;張大龍;戴飛;李麗麗;;基于LabVIEW的微孔鉆削刀具信號(hào)監(jiān)測(cè)系統(tǒng)的設(shè)計(jì)[J];甘肅農(nóng)業(yè)大學(xué)學(xué)報(bào);2013年06期

6 劉路;王太勇;蔣永翔;胡淼;寧倩;;基于超球面支持向量機(jī)的刀具磨損狀態(tài)識(shí)別[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2011年01期

7 聶鵬;諶鑫;徐濤;孫寶林;;基于小波神經(jīng)網(wǎng)絡(luò)的航空刀具磨損狀態(tài)識(shí)別[J];北京航空航天大學(xué)學(xué)報(bào);2011年01期

8 田榮鑫;史耀耀;楊振朝;梁永收;石凱;;TC17鈦合金銑削刀具磨損對(duì)殘余應(yīng)力影響研究[J];航空制造技術(shù);2011年Z1期

9 呂俊杰;王杰;王玫;吳越;;基于SOM和HMM結(jié)合的刀具磨損狀態(tài)監(jiān)測(cè)研究[J];中國(guó)機(jī)械工程;2010年13期

10 馬旭;陳捷;;數(shù)控銑床刀具磨損量與主軸電流關(guān)系研究[J];組合機(jī)床與自動(dòng)化加工技術(shù);2009年10期

相關(guān)博士學(xué)位論文 前4條

1 李威霖;車銑刀具磨損狀態(tài)監(jiān)測(cè)及預(yù)測(cè)關(guān)鍵技術(shù)研究[D];西南交通大學(xué);2013年

2 陳侃;基于多模型決策融合的刀具磨損狀態(tài)監(jiān)測(cè)系統(tǒng)關(guān)鍵技術(shù)研究[D];西南交通大學(xué);2012年

3 關(guān)山;基于聲發(fā)射信號(hào)多特征分析與融合的刀具磨損分類與預(yù)測(cè)技術(shù)[D];吉林大學(xué);2011年

4 高宏力;切削加工過程中刀具磨損的智能監(jiān)測(cè)技術(shù)研究[D];西南交通大學(xué);2005年

相關(guān)碩士學(xué)位論文 前2條

1 劉冬偉;基于多傳感器融合的銑刀破損監(jiān)測(cè)方法研究[D];哈爾濱工業(yè)大學(xué);2015年

2 秦龍;鈦合金切削加工中顫振與刀具磨損的試驗(yàn)研究[D];大連理工大學(xué);2009年

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