本文選題：表面粗糙度 + 紋理分析��； 參考：《沈陽建筑大學(xué)》2015年碩士論文

【摘要】：表面粗糙度是評定零件表面質(zhì)量的重要指標,它直接影響到零件的使用性能、安全和壽命,尤其對于具有特殊功能(密封、相對移動等)的零件更是如此。因此,快速準確、無損地檢測零件工作表面的粗糙度對于零件的正常使用性能和系統(tǒng)的安全性具有重要意義。本文基于計算機視覺理論,采用圖像紋理分析方法,實現(xiàn)了對車削工件表面粗糙度的非接觸式無損檢測。本文研究的主要內(nèi)容為:(1)搭建了以VHX-1000型超景深三維顯微系統(tǒng)為核心的測量系統(tǒng)硬件平臺,實現(xiàn)了車削工件表面清晰顯微圖像的獲取。(2)基于灰度共生矩陣方法(GLCM),對車削工件表面圖像進行了紋理統(tǒng)計分析和特征提取。首先,根據(jù)車削表面圖像特征,確定了灰度共生矩陣的最優(yōu)構(gòu)造參數(shù),為后續(xù)的特征提取提供準確的數(shù)據(jù)。其次,根據(jù)車削原理和車削痕跡圖像特征,提取并分析了基于灰度共生矩陣的14個統(tǒng)計特征參數(shù),分別為角二階矩、對比度、相關(guān)性、差分矩、逆差分矩、和平均、和方差、和熵、熵、差方差、差熵、相關(guān)信息測度Ⅰ、相關(guān)信息測度Ⅱ和最大相關(guān)系數(shù)。建立了灰度共生矩陣統(tǒng)計特征參數(shù)和對應(yīng)的表面粗糙度的關(guān)系模型數(shù)據(jù)庫。(3)采用多元回歸分析方法構(gòu)建了車削工件表面粗糙度的檢測模型,實現(xiàn)了車削工件表面粗糙度的定量計算。分別建立了多元線性回歸檢測模型與多元非線性回歸檢測模型,擬合了工件表面紋理特征參數(shù)與工件表面粗糙度評定參數(shù)Ra映射關(guān)系的數(shù)學(xué)表達式。通過測試樣本檢驗,兩種多元回歸分析方法均具有較高的檢測精度,能夠滿足表面粗糙度測量的精度要求。實驗表明,非線性多元回歸檢測模型檢測精度優(yōu)于線性多元回歸檢測模型。(4)采用BP神經(jīng)網(wǎng)絡(luò)算法,建立了車削表面粗糙度的檢測模型。以車削表面圖像紋理特征參數(shù)為輸入量,對應(yīng)的表面粗糙度評定值Ra為期望輸出,構(gòu)建了神經(jīng)網(wǎng)絡(luò)檢測模型。實驗結(jié)果表明,BP神經(jīng)網(wǎng)絡(luò)檢測模型具有較高的檢測精度,且其檢測精度高于多元回歸檢測模型。(5)以MARLAB軟件為開發(fā)平臺,完成了車削表面粗糙度檢測系統(tǒng)的軟件設(shè)計,并開發(fā)了圖形用戶界面。最后,應(yīng)用該檢測系統(tǒng)對檢測模型進行了實驗研究,并將該檢測結(jié)果與傳統(tǒng)探針式測量結(jié)果進行了對比分析。結(jié)果表明,本文檢測模型的檢測平均誤差率均在允許范圍內(nèi),滿足測量要求,可以實現(xiàn)車削表面粗糙度的快速準確、無損檢測。
[Abstract]:Surface roughness is an important index to evaluate the surface quality of parts, which directly affects the performance, safety and life of parts, especially for parts with special functions (seal, relative movement, etc.). Therefore, it is very important to detect the roughness of the working surface quickly, accurately and nondestructive for the normal performance of the parts and the safety of the system. Based on the theory of computer vision and the method of image texture analysis, the non-contact nondestructive testing of the surface roughness of turning workpiece is realized in this paper. The main content of this paper is: (1) the hardware platform of measurement system based on VHX-1000 type hyper-depth of field 3D microscope system is set up. Based on the method of gray level co-occurrence matrix (GLCM), texture statistic analysis and feature extraction of turning workpiece surface image are carried out. Firstly, according to the feature of turning surface image, the optimal construction parameters of gray level co-occurrence matrix are determined, which can provide accurate data for the subsequent feature extraction. Secondly, according to turning principle and turning trace image feature, 14 statistical characteristic parameters based on gray level co-occurrence matrix are extracted and analyzed, which are angular second order moment, contrast, correlation, differential moment, deficit moment, average, and variance. Sum entropy, differential variance, differential entropy, correlation information measure 鈪，

本文編號：1873096