【摘要】：排屑鉆與普通鉆削加工相比,可提高鉆削加工質(zhì)量,顯著改善孔加工表面粗糙度質(zhì)量。但排屑鉆加工過程和普通鉆削一樣都處于半封閉或者封閉環(huán)境,孔加工表面粗糙度也難以檢測(cè)和分析。本文擬結(jié)合鉆削工藝參數(shù)和監(jiān)測(cè)信號(hào)特征,開展排屑鉆孔加工表面粗糙度預(yù)測(cè)研究。所開展的主要工作包括監(jiān)測(cè)平臺(tái)搭建、信號(hào)消噪處理、工藝參數(shù)和監(jiān)測(cè)信號(hào)特征對(duì)粗糙度的影響規(guī)律、預(yù)測(cè)模型的建立與驗(yàn)證等方面。(1)排屑鉆監(jiān)控平臺(tái)搭建與數(shù)據(jù)采集。搭建排屑鉆監(jiān)控平臺(tái),采集排屑鉆加工過程中的振動(dòng)信號(hào)、聲發(fā)射信號(hào),以及所加工孔壁粗糙度值,并采用最小二乘擬合的方法對(duì)信號(hào)進(jìn)行趨勢(shì)項(xiàng)處理。(2)信號(hào)消噪處理。針對(duì)在鉆削加工噪聲背景下振動(dòng)信號(hào)特征識(shí)別和提取困難的問題,提出了一種小波包分頻譜減去噪方法。根據(jù)鉆削信號(hào)在時(shí)頻域特點(diǎn),首先將鉆削前機(jī)床空轉(zhuǎn)信號(hào)視為監(jiān)測(cè)信號(hào)的“加性噪聲”;然后,采用小波包分解將“加性噪聲”和監(jiān)測(cè)信號(hào)進(jìn)行分頻處理,確定各頻帶幀數(shù);最后,對(duì)各個(gè)子頻帶內(nèi)“加性噪聲”的相應(yīng)頻帶進(jìn)行譜減處理,再重構(gòu)鉆削振動(dòng)信號(hào)。(3)工藝參數(shù)和監(jiān)測(cè)信號(hào)特征對(duì)粗糙度的影響規(guī)律。依據(jù)所采集的實(shí)驗(yàn)數(shù)據(jù),首先分析了不同工藝參數(shù)對(duì)監(jiān)測(cè)信號(hào)特征以及孔壁粗糙度的影響規(guī)律。然后通過方差分析的方法研究了不同工藝參數(shù)對(duì)監(jiān)測(cè)信號(hào)特征和表面粗糙度影響的顯著性。最后,分析了監(jiān)測(cè)信號(hào)特征與孔壁表面粗糙度的對(duì)應(yīng)關(guān)系。(4)粗糙度預(yù)測(cè)模型建立與驗(yàn)證。首先確定了神經(jīng)網(wǎng)絡(luò)的輸入層與輸出層節(jié)點(diǎn)數(shù),然后針對(duì)BP神經(jīng)網(wǎng)絡(luò)隱含層節(jié)點(diǎn)數(shù)無法確定的問題,采用動(dòng)態(tài)調(diào)節(jié)隱含層節(jié)點(diǎn)數(shù)的方法,對(duì)比不同結(jié)構(gòu)預(yù)測(cè)值的準(zhǔn)確度,確定最優(yōu)網(wǎng)絡(luò)結(jié)構(gòu)。最后,通過對(duì)試驗(yàn)樣本進(jìn)行仿真分析,對(duì)粗糙度預(yù)測(cè)模型的有效性進(jìn)行驗(yàn)證。理論分析和實(shí)驗(yàn)結(jié)果表明:采用本文所建立的粗糙度預(yù)測(cè)模型,能夠有效預(yù)測(cè)排屑鉆表面粗糙度。同時(shí)該方法可有效克服傳統(tǒng)粗糙度檢測(cè)采用人工抽檢所導(dǎo)致的漏檢、檢測(cè)效率不高等缺點(diǎn),為實(shí)現(xiàn)排屑鉆粗糙度預(yù)測(cè)提供了新的方法和理論基礎(chǔ)。
[Abstract]:Compared with common drilling, chip removal drill can improve the quality of drilling and improve the surface roughness of hole machining. However, the process of chip removal drilling is in a semi-closed or closed environment, and the surface roughness of hole machining is also difficult to detect and analyze. In this paper, combining with the parameters of drilling process and the characteristics of monitoring signals, the prediction of surface roughness of chip removal drilling is carried out. The main work includes the construction of monitoring platform, signal de-noising processing, the influence of process parameters and monitoring signal characteristics on roughness, the establishment and verification of prediction model and so on. (1) the construction of monitoring platform for chip removal drill and data acquisition. The monitoring platform of chip removal drill is set up to collect vibration signal, acoustic emission signal and the roughness value of the hole wall in the process of chip removal drilling. The least square fitting method is used to process the trend term of the signal. (2) the signal is de-noised. In order to solve the problem of difficult recognition and extraction of vibration signals in the background of drilling noise, a wavelet packet spectrum division subtract method is proposed. According to the characteristics of drilling signal in time and frequency domain, the machine tool idle signal before drilling is regarded as the "additive noise" of the monitoring signal, and then the "additive noise" and the monitoring signal are processed by using wavelet packet decomposition to determine the frame number of each frequency band. Finally, the corresponding frequency band of "additive noise" in each sub-band is subtracted and then the drilling vibration signal is reconstructed. (3) the influence of technological parameters and monitoring signal characteristics on roughness. Based on the experimental data collected, the influence of different process parameters on the characteristics of the monitoring signal and the roughness of the hole wall was analyzed. Then, the effects of different process parameters on the characteristics of monitoring signals and surface roughness were studied by ANOVA. Finally, the relationship between the characteristics of the monitoring signal and the surface roughness of the hole wall is analyzed. (4) the prediction model of roughness is established and verified. Firstly, the number of nodes in the input layer and the output layer of the neural network is determined. Then, aiming at the problem that the number of hidden layer nodes in the BP neural network cannot be determined, the method of dynamically adjusting the number of nodes in the hidden layer is adopted to compare the accuracy of the predicted values of different structures. The optimal network structure is determined. Finally, the validity of the roughness prediction model is verified by the simulation analysis of the test samples. The theoretical analysis and experimental results show that the roughness prediction model established in this paper can effectively predict the surface roughness of chip removal drills. At the same time, this method can effectively overcome the shortcomings of traditional roughness detection caused by manual sampling, such as low detection efficiency and so on. It provides a new method and theoretical basis for the prediction of chip removal drill roughness.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG52

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 周小龍;姜振海;馬風(fēng)雷;;基于改進(jìn)HHT的微弱故障信號(hào)特征提取方法[J];東北電力大學(xué)學(xué)報(bào);2016年05期

2 劉森;李靜;高華玨;沈南燕;;鈦合金磨削中砂輪磨損狀態(tài)在線監(jiān)測(cè)技術(shù)研究[J];精密制造與自動(dòng)化;2015年04期

3 王依諾;陳從建;;基于ABAQUS的小孔鉆削加工仿真（英文）[J];Journal of Measurement Science and Instrumentation;2015年03期

4 張保平;關(guān)世璽;張雪冬;王斌;田甜;;基于模糊評(píng)判的深孔刀具選擇及切削參數(shù)優(yōu)化[J];制造業(yè)自動(dòng)化;2015年15期

5 劉毅娟;雷鳴;何e，

本文編號(hào)：2452168