微間隙焊縫磁光成像識別模型研究
發(fā)布時間：2018-07-31 14:41
【摘要】：激光焊接技術(shù)具有激光功率大、光斑直徑小、光束質(zhì)量優(yōu)良、熱影響區(qū)域小、大深寬比、可實現(xiàn)異種材料之間的連接并且焊接質(zhì)量優(yōu)良等優(yōu)點。激光焊接過程中,控制激光束實時準(zhǔn)確地對準(zhǔn)焊縫中心位置是保證獲取良好焊件的關(guān)鍵。由于激光束光斑直徑小(一般小于200μm),對焊縫間隙大小敏感,要求焊縫間隙盡可能小。傳統(tǒng)的結(jié)構(gòu)光視覺法利用結(jié)構(gòu)光橫跨于焊縫位置所產(chǎn)生的突變特征實現(xiàn)焊縫識別,但無法識別小于0.10mm間隙的焊縫。在實際工業(yè)焊接現(xiàn)場存在大量的煙霧、飛濺及等離子體等干擾影響,普通攝像機無法清晰捕捉焊接區(qū)域熔池和微間隙焊縫位置的準(zhǔn)確信息,且激光焊接過程中存在劇烈的熱能轉(zhuǎn)換效應(yīng),對焊接工藝參數(shù)及工件的裝配、固定精度要求極高,微小的變化即可導(dǎo)致嚴(yán)重的焊接缺陷甚至報廢,因此,精確控制激光束使其始終對正并跟蹤焊縫是保證激光焊接質(zhì)量的前提。論文綜合比較了現(xiàn)有焊縫識別與跟蹤方法的優(yōu)缺點,結(jié)合實際工業(yè)需求,重點研究激光焊接微間隙(不大于0.20mm)焊縫磁光成像焊縫識別技術(shù)。針對激光焊接等厚、無坡口、緊密對接、肉眼難以分辨的微間隙焊縫,根據(jù)法拉第磁光效應(yīng)原理構(gòu)成磁光成像傳感器獲取焊縫磁光圖像,參與設(shè)計并搭建了激光焊接不銹鋼緊密對接焊縫磁光成像試驗平臺(第二章),研究激光焊接前微間隙焊縫的磁光成像特征和機理,實現(xiàn)微間隙焊縫位置檢測,為后續(xù)激光焊接過程中焊縫識別與跟蹤奠定基礎(chǔ),保證激光焊接質(zhì)量。首先,將焊件放置于伺服工作臺上,在焊縫下方放置磁場發(fā)生器,通過調(diào)節(jié)磁場發(fā)生器的勵磁電壓大小,改變焊縫周圍的感應(yīng)電流及感應(yīng)磁場強度。根據(jù)法拉第電磁感應(yīng)效應(yīng)及法拉第磁光效應(yīng),當(dāng)渦流在流動路徑上存在焊縫間隙時,其流動受到影響,渦流會在焊縫位置處產(chǎn)生畸變,畸變的渦流會產(chǎn)生畸變的渦流磁場,從而引起該位置處垂直磁場分布的變化。通過磁光傳感器將渦流磁場變化轉(zhuǎn)換成相應(yīng)的光強變化,實現(xiàn)焊縫的實時成像,研究微間隙焊縫磁光成像特征與焊縫位置的關(guān)聯(lián)。結(jié)果表明,改變勵磁電壓、磁光傳感器與焊件距離、焊接速度、焊縫間隙大小等參數(shù),對微間隙焊縫磁光成像的變化較為敏感。其次,分別利用微間隙焊縫磁光圖像特征(灰度特征、灰度梯度特征、彩色空間特征和紋理特征),探索各特征與微間隙焊縫位置之間的規(guī)律。分析微間隙焊縫磁光圖像灰度和灰度梯度分布特點,通過全局閾值和邊緣算子,可以提取出焊縫過渡帶輪廓并將其中心位置認(rèn)作焊縫中心位置坐標(biāo),但閾值的選取不具有通用性,當(dāng)微間隙焊縫磁光成像試驗參數(shù)改變,需要多次反復(fù)試驗選取合適的閾值。利用焊縫磁光圖像的彩色空間特征,在RGB和HSV顏色空間分別計算各彩色分量圖的灰度分布特征后提取焊縫位置坐標(biāo),彩色顏色空間焊縫位置測量精度較灰度圖的焊縫位置測量精度高。最后,分析微間隙焊縫磁光圖像序列特性,利用圖像序列中各像素數(shù)據(jù)的時域變化和相關(guān)性確定各像素位置的運動和焊縫位置坐標(biāo)。研究了光流法和梯度矢量流模型在微間隙焊縫磁光圖像識別中的應(yīng)用。另外,利用人工神經(jīng)網(wǎng)絡(luò)、粒子濾波和卡爾曼濾波算法構(gòu)建描述焊縫位置的識別模型,最終實現(xiàn)微間隙焊縫識別、跟蹤和預(yù)測,保證焊接質(zhì)量。通過這一系列的研究工作,取得了如下主要研究成果：(1)研究焊縫磁光成像與焊縫實際狀況及其它因素間的內(nèi)在關(guān)系。對于一個特定的磁光傳感器,影響微間隙焊縫磁光成像的主要因素為法拉第磁光效應(yīng),相關(guān)因素包括：勵磁電壓、激勵線圈與焊件的距離(提離度)、焊縫形態(tài)以及焊接速度等。試驗表明：針對同一微間隙焊縫,改變勵磁電壓大小,焊縫周圍的磁感應(yīng)強度隨之改變,焊縫位置處磁感應(yīng)強度介于兩邊磁場(N極和S極)強度的對稱中心。焊縫磁光圖像中的焊縫過渡帶區(qū)域隨勵磁電壓改變而上下移動,但對于兩個不同的磁場強度下所獲得的磁光焊縫位置的偏移量是恒定的,即對于特定的磁場強度環(huán)境下此偏移可以忽略不計,不影響實際的焊縫識別和跟蹤。針對同一微間隙焊縫,改變焊接速度,磁光成像焊縫位置測量值基本恒定不變,即焊接速度僅僅對工件的焊透情況和圖像采集幀數(shù)有影響、對微間隙焊縫位置的檢測無影響。同一勵磁電壓下,改變焊縫間隙大小,間隙越大,磁光成像中焊縫位置過渡區(qū)域越小,焊縫區(qū)域成像越清晰。同一焊縫間隙和勵磁電壓下,磁光傳感器與焊件距離(提離度)越近,磁光成像中焊縫位置過渡區(qū)域越小,焊縫區(qū)域成像越清楚。(2)微間隙焊縫磁光圖像特征的提取與分析。微間隙焊縫磁光圖像特征包括：灰度特征、灰度梯度特征、紋理特征和圖像序列特征。焊縫介于兩塊母材的中間,垂直掃描焊縫位置,焊縫左右兩邊母材的灰度分布存在明顯的差別,可利用灰度分布在焊縫位置處的差異性檢測焊縫中心位置。掃描微間隙焊縫磁光圖像所有列的灰度梯度分布曲線,尋找灰度梯度極大值所對應(yīng)的行作為焊縫過渡帶的上、下邊緣坐標(biāo),計算焊縫過渡帶上下邊緣的中心坐標(biāo)作為焊縫位置坐標(biāo)。分別在圖像的焊縫區(qū)域和母材區(qū)域(焊縫上、下區(qū)域的母材)提取三個相同尺寸的子圖像,計算各個子圖像的紋理特征(包括：平均亮度、標(biāo)準(zhǔn)差、平滑度、三階矩、一致性、熵1、能量、相關(guān)、熵2、逆差矩等),利用紋理特征差異,將焊縫和母材區(qū)域進(jìn)行分割。(3)微間隙焊縫磁光成像焊縫位置檢測。采集微間隙焊縫磁光連續(xù)圖像序列,利用采樣時間重疊的圖像序列光流場分布,根據(jù)圖像序列的時域特性和相關(guān)性,提取光流場中的u分量峰值處對應(yīng)的像素點作為焊縫位置。H-S(Horn,Schunck)光流法焊縫識別方法所計算出的焊縫位置大部分與焊縫位置實際值吻合,由于焊縫磁光圖像存在噪聲干擾影響,光流法焊縫提取方法在若干部位出現(xiàn)較大的波動,在工程實踐中,可根據(jù)工程經(jīng)驗,設(shè)定一個閾值將波動較大的值視為粗大誤差,予以剔除。同時,分析了梯度矢量流場在微間隙焊縫磁光圖像分割中的應(yīng)用,將感興趣區(qū)域的焊縫邊緣看作具有能量的不間斷曲線,在控制點所具有的能量控制下使得活動輪廓產(chǎn)生變形,活動輪廓在控制點的內(nèi)力、外力以及圖像力的共同作用下,向焊縫目標(biāo)區(qū)域進(jìn)行伸縮,最終實現(xiàn)焊縫位置檢測。(4)微間隙焊縫位置識別模型的建立。分別建立了基于BP神經(jīng)網(wǎng)絡(luò)、基于Elman神經(jīng)網(wǎng)絡(luò)、卡爾曼濾波預(yù)測焊縫位置模型。設(shè)計了一種前饋型神經(jīng)網(wǎng)絡(luò)焊縫位置預(yù)測模型,通過對前一時刻的焊縫位置和焊縫位置差值來估算當(dāng)前時刻焊縫位置。比較了BP神經(jīng)網(wǎng)絡(luò)和Elman網(wǎng)絡(luò)焊縫位置預(yù)測的精度。結(jié)果表明：BP神經(jīng)網(wǎng)絡(luò)的預(yù)測能力比Elman神經(jīng)網(wǎng)絡(luò)更強,能有效地進(jìn)行焊縫位置的預(yù)測,且測量精度優(yōu)于Elman網(wǎng)絡(luò)。利用Kalman濾波對微間隙焊縫進(jìn)行跟蹤和預(yù)測,在已知焊縫位置測量信息的前提下,獲取系統(tǒng)狀態(tài)的最優(yōu)估計,最后實現(xiàn)焊縫位置的最佳預(yù)測和估計。卡爾曼濾波后,噪聲干擾得到較大地抑制,能有效提高焊縫跟蹤精度。
[Abstract]:Laser welding technology has the advantages of large laser power, small spot diameter, good beam quality, small heat affected area and large depth width ratio. It can realize the connection between different kinds of materials and excellent welding quality. In the process of laser welding, it is the key to ensure the accurate alignment of the weld center by controlling the laser beam in real time. The light spot diameter of the beam is small (generally less than 200 m), which is sensitive to the size of the weld gap, and requires that the weld gap be as small as possible. The traditional structure optical vision method uses the abrupt characteristics of the structure light across the weld position to realize the weld recognition, but can not identify the weld less than the 0.10mm gap. There are a lot of smoke in the actual industrial welding site. With the influence of the interference of splash and plasma, the ordinary camera can not clearly capture the accurate information of the weld pool and the micro gap weld position, and there is a severe heat transfer effect in the process of laser welding. The welding process parameters and the assembly of the workpiece and the fixed precision are very high, and the small change can lead to serious welding defects. As a result, it is a prerequisite for accurate control of the laser beam to make it always positive and tracking weld is the prerequisite for ensuring the quality of laser welding. This paper compares the advantages and disadvantages of the existing welding seam recognition and tracking methods. Combined with the actual industrial demand, the paper focuses on the study of the laser welding micro gap (less than 0.20mm) weld recognition technology of magneto optic imaging weld. Light welding with equal thickness, no slope, close butt, undistinguishable micro gap weld, magneto-optical imaging sensor based on Faraday magneto-optical effect principle to obtain magnetic and optical images of weld, and to design and build a laser welded stainless steel close butt weld magneto-optical imaging test flat (second chapter), to study the micro gap weld before laser welding. The characteristics and mechanism of magneto-optical imaging can be used to detect the position of the micro gap weld. It lays the foundation for the seam recognition and tracking in the follow-up laser welding process and ensures the quality of the laser welding. First, the welding parts are placed on the servo worktable, and the magnetic generator is placed under the weld, and the excitation voltage is changed around the weld line by adjusting the excitation voltage of the magnetic field generator. According to the Faraday electromagnetic induction effect and the magnetic field intensity of the induction magnetic field, the flow is affected when the eddy current exists in the weld gap on the flow path, and the eddy will distort at the weld position, and the distorted eddy current will produce the distorted eddy current magnetic field, resulting in the distribution of the vertical magnetic field at this position. Change. The change of eddy magnetic field change into corresponding light intensity change through magneto-optical sensor, real-time imaging of weld seam, the correlation between magneto optic imaging characteristics of micro gap weld and weld position. The results show that the excitation voltage, the distance between magneto optic sensor and welding part, welding speed, weld gap size and so on, and the micro gap weld magnetism The change of optical imaging is more sensitive. Secondly, the characteristics of the magneto-optical image of the micro gap weld (gray feature, gray gradient feature, color space feature and texture feature) are used to explore the regularity between the features and the position of the micro gap weld. The characteristics of the gray degree and gray gradient distribution of the magneto optic images of the micro gap weld are analyzed, and the global threshold and edge are used to get the edge of the weld. The edge operator can extract the contour of the weld transition zone and recognize its central position as the position coordinates of the weld center, but the selection of the threshold is not universal. When the magnetic imaging test parameters of the micro gap weld are changed, many repeated tests are needed to select the appropriate threshold. Using the color space features of the weld magneto-optical image, the color space of RGB and HSV is empty. The weld position coordinates are extracted after calculating the gray distribution characteristics of each color component, and the precision of the weld position measurement accuracy is higher than that of the gray map. Finally, the characteristics of the magneto-optical image sequence of the micro gap weld are analyzed, and each pixel is determined by the time domain change and correlation in the image sequence. The application of optical flow method and gradient vector flow model to the recognition of magneto optical image in micro gap weld is studied. In addition, the recognition model of weld position is constructed by artificial neural network, particle filter and Calman filtering algorithm. Finally, the recognition, tracking and prediction of micro gap weld seam can be realized, and the welding is ensured. Quality. Through this series of research work, the main achievements are obtained as follows: (1) the study of the intrinsic relationship between the weld magneto-optical imaging and the actual conditions of the weld and other factors. For a specific magnetic and optical sensor, the main factor affecting the magneto-optical imaging of the micro gap welds is the Faraday magneto-optical effect, and the related factors include the excitation electricity. Pressure, the distance (lift off degree), weld shape and welding speed of the excitation coil and the weldment. The test shows that the magnetic induction intensity around the weld changes with the size of the excitation voltage changed for the same micro gap weld, and the magnetic induction intensity at the weld position is at the symmetry center of the magnetic field (N pole and S pole) on both sides. The transition zone of the weld transition zone moves up and down with the change of the excitation voltage, but the offset of the magneto optic weld position obtained under two different magnetic field strength is constant, that is, the offset can be ignored in a specific magnetic field intensity environment and does not affect the actual weld recognition and tracking. Speed, the measurement value of the weld position of the magneto optic imaging is basically constant, that is, the welding speed is only influenced by the penetration of the workpiece and the number of the image collection frames, and there is no influence on the detection of the position of the micro gap weld. The more clearer the image is. The closer the distance between the magneto-optical sensor and the weldment is, the closer the distance between the magneto-optical sensor and the weldment is, the smaller the weld position transition region is, the more clear the image of the weld area is. (2) the extraction and analysis of the characteristics of the magneto-optical image of the micro gap weld. The characteristics of the micro gap welding seam magneto optical image include the gray feature and the grayscale gradient special. Signs, texture features and image sequence characteristics. The weld is in the middle of two pieces of parent material, and the weld position is scanned vertically. There is a significant difference in the gray distribution of the left and right sides of the weld. The gray distribution at the weld position can be used to detect the position of the weld center. The gray gradient distribution of all the magneto optical images of the weld is scanned. Curve, the line corresponding to the maximum value of grayscale gradient is used as the upper and lower edge coordinates of the weld transition zone, and the center coordinates of the upper and lower edges of the weld transition zone are calculated as the position coordinates of the weld. Three subimages of the same size are extracted in the weld area of the image and the base material on the base of the weld, and the sub images are calculated. The texture features (including: average brightness, standard deviation, smoothness, three moment, consistency, entropy 1, energy, correlation, entropy 2, inverse moment, etc.), use the difference of the texture features to divide the weld and the base area. (3) the detection of the weld position of the micro gap weld magneto optical imaging. The continuous image sequence of the micro gap weld is collected, and the overlap time is overlapped. According to the time domain characteristics and correlation of image sequence, the corresponding pixel points at the peak of the U component in the optical flow field are extracted as the weld position.H-S (Horn, Schunck) method for welding seam recognition. Most of the weld position is in accordance with the actual value of the weld position, because the magnetic and optical image of the weld is noisy. The welding seam extraction method of the optical flow method has a large fluctuation in several parts. In engineering practice, according to the engineering experience, a threshold value is set to remove the fluctuating value as a rough error. Meanwhile, the application of the gradient vector flow field in the segmentation of the magneto-optical image in the micro gap weld is analyzed, and the weld seam in the region of interest is used. The edge is regarded as an uninterrupted curve with energy. Under the control point energy control, the active contour is deformed. The active contour is expanded to the weld target area under the joint action of the control point, external force and image force, and the weld position detection is finally realized. (4) the establishment of the recognition model of the micro gap weld position. Based on the BP neural network, based on the Elman neural network and the Calman filter to predict the weld position model, a prediction model for the weld position of the feedforward neural network is designed. The weld position at the present time is estimated by the difference between the weld position and the weld position at the first time. The weld position of the BP neural network and the Elman network is compared. The results show that the prediction ability of the BP neural network is stronger than that of the Elman neural network, and it can predict the weld position effectively, and the measurement accuracy is better than that of the Elman network. The Kalman filter is used to track and predict the micro gap weld, and the optimal estimation of the system state is obtained on the premise of the known weld position measurement information. Finally, the optimal prediction and estimation of the weld position are realized. After Kalman filter, the noise interference is greatly suppressed and the tracking accuracy is effectively improved.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TG441.7;TP391.41
本文編號：2155907
[Abstract]:Laser welding technology has the advantages of large laser power, small spot diameter, good beam quality, small heat affected area and large depth width ratio. It can realize the connection between different kinds of materials and excellent welding quality. In the process of laser welding, it is the key to ensure the accurate alignment of the weld center by controlling the laser beam in real time. The light spot diameter of the beam is small (generally less than 200 m), which is sensitive to the size of the weld gap, and requires that the weld gap be as small as possible. The traditional structure optical vision method uses the abrupt characteristics of the structure light across the weld position to realize the weld recognition, but can not identify the weld less than the 0.10mm gap. There are a lot of smoke in the actual industrial welding site. With the influence of the interference of splash and plasma, the ordinary camera can not clearly capture the accurate information of the weld pool and the micro gap weld position, and there is a severe heat transfer effect in the process of laser welding. The welding process parameters and the assembly of the workpiece and the fixed precision are very high, and the small change can lead to serious welding defects. As a result, it is a prerequisite for accurate control of the laser beam to make it always positive and tracking weld is the prerequisite for ensuring the quality of laser welding. This paper compares the advantages and disadvantages of the existing welding seam recognition and tracking methods. Combined with the actual industrial demand, the paper focuses on the study of the laser welding micro gap (less than 0.20mm) weld recognition technology of magneto optic imaging weld. Light welding with equal thickness, no slope, close butt, undistinguishable micro gap weld, magneto-optical imaging sensor based on Faraday magneto-optical effect principle to obtain magnetic and optical images of weld, and to design and build a laser welded stainless steel close butt weld magneto-optical imaging test flat (second chapter), to study the micro gap weld before laser welding. The characteristics and mechanism of magneto-optical imaging can be used to detect the position of the micro gap weld. It lays the foundation for the seam recognition and tracking in the follow-up laser welding process and ensures the quality of the laser welding. First, the welding parts are placed on the servo worktable, and the magnetic generator is placed under the weld, and the excitation voltage is changed around the weld line by adjusting the excitation voltage of the magnetic field generator. According to the Faraday electromagnetic induction effect and the magnetic field intensity of the induction magnetic field, the flow is affected when the eddy current exists in the weld gap on the flow path, and the eddy will distort at the weld position, and the distorted eddy current will produce the distorted eddy current magnetic field, resulting in the distribution of the vertical magnetic field at this position. Change. The change of eddy magnetic field change into corresponding light intensity change through magneto-optical sensor, real-time imaging of weld seam, the correlation between magneto optic imaging characteristics of micro gap weld and weld position. The results show that the excitation voltage, the distance between magneto optic sensor and welding part, welding speed, weld gap size and so on, and the micro gap weld magnetism The change of optical imaging is more sensitive. Secondly, the characteristics of the magneto-optical image of the micro gap weld (gray feature, gray gradient feature, color space feature and texture feature) are used to explore the regularity between the features and the position of the micro gap weld. The characteristics of the gray degree and gray gradient distribution of the magneto optic images of the micro gap weld are analyzed, and the global threshold and edge are used to get the edge of the weld. The edge operator can extract the contour of the weld transition zone and recognize its central position as the position coordinates of the weld center, but the selection of the threshold is not universal. When the magnetic imaging test parameters of the micro gap weld are changed, many repeated tests are needed to select the appropriate threshold. Using the color space features of the weld magneto-optical image, the color space of RGB and HSV is empty. The weld position coordinates are extracted after calculating the gray distribution characteristics of each color component, and the precision of the weld position measurement accuracy is higher than that of the gray map. Finally, the characteristics of the magneto-optical image sequence of the micro gap weld are analyzed, and each pixel is determined by the time domain change and correlation in the image sequence. The application of optical flow method and gradient vector flow model to the recognition of magneto optical image in micro gap weld is studied. In addition, the recognition model of weld position is constructed by artificial neural network, particle filter and Calman filtering algorithm. Finally, the recognition, tracking and prediction of micro gap weld seam can be realized, and the welding is ensured. Quality. Through this series of research work, the main achievements are obtained as follows: (1) the study of the intrinsic relationship between the weld magneto-optical imaging and the actual conditions of the weld and other factors. For a specific magnetic and optical sensor, the main factor affecting the magneto-optical imaging of the micro gap welds is the Faraday magneto-optical effect, and the related factors include the excitation electricity. Pressure, the distance (lift off degree), weld shape and welding speed of the excitation coil and the weldment. The test shows that the magnetic induction intensity around the weld changes with the size of the excitation voltage changed for the same micro gap weld, and the magnetic induction intensity at the weld position is at the symmetry center of the magnetic field (N pole and S pole) on both sides. The transition zone of the weld transition zone moves up and down with the change of the excitation voltage, but the offset of the magneto optic weld position obtained under two different magnetic field strength is constant, that is, the offset can be ignored in a specific magnetic field intensity environment and does not affect the actual weld recognition and tracking. Speed, the measurement value of the weld position of the magneto optic imaging is basically constant, that is, the welding speed is only influenced by the penetration of the workpiece and the number of the image collection frames, and there is no influence on the detection of the position of the micro gap weld. The more clearer the image is. The closer the distance between the magneto-optical sensor and the weldment is, the closer the distance between the magneto-optical sensor and the weldment is, the smaller the weld position transition region is, the more clear the image of the weld area is. (2) the extraction and analysis of the characteristics of the magneto-optical image of the micro gap weld. The characteristics of the micro gap welding seam magneto optical image include the gray feature and the grayscale gradient special. Signs, texture features and image sequence characteristics. The weld is in the middle of two pieces of parent material, and the weld position is scanned vertically. There is a significant difference in the gray distribution of the left and right sides of the weld. The gray distribution at the weld position can be used to detect the position of the weld center. The gray gradient distribution of all the magneto optical images of the weld is scanned. Curve, the line corresponding to the maximum value of grayscale gradient is used as the upper and lower edge coordinates of the weld transition zone, and the center coordinates of the upper and lower edges of the weld transition zone are calculated as the position coordinates of the weld. Three subimages of the same size are extracted in the weld area of the image and the base material on the base of the weld, and the sub images are calculated. The texture features (including: average brightness, standard deviation, smoothness, three moment, consistency, entropy 1, energy, correlation, entropy 2, inverse moment, etc.), use the difference of the texture features to divide the weld and the base area. (3) the detection of the weld position of the micro gap weld magneto optical imaging. The continuous image sequence of the micro gap weld is collected, and the overlap time is overlapped. According to the time domain characteristics and correlation of image sequence, the corresponding pixel points at the peak of the U component in the optical flow field are extracted as the weld position.H-S (Horn, Schunck) method for welding seam recognition. Most of the weld position is in accordance with the actual value of the weld position, because the magnetic and optical image of the weld is noisy. The welding seam extraction method of the optical flow method has a large fluctuation in several parts. In engineering practice, according to the engineering experience, a threshold value is set to remove the fluctuating value as a rough error. Meanwhile, the application of the gradient vector flow field in the segmentation of the magneto-optical image in the micro gap weld is analyzed, and the weld seam in the region of interest is used. The edge is regarded as an uninterrupted curve with energy. Under the control point energy control, the active contour is deformed. The active contour is expanded to the weld target area under the joint action of the control point, external force and image force, and the weld position detection is finally realized. (4) the establishment of the recognition model of the micro gap weld position. Based on the BP neural network, based on the Elman neural network and the Calman filter to predict the weld position model, a prediction model for the weld position of the feedforward neural network is designed. The weld position at the present time is estimated by the difference between the weld position and the weld position at the first time. The weld position of the BP neural network and the Elman network is compared. The results show that the prediction ability of the BP neural network is stronger than that of the Elman neural network, and it can predict the weld position effectively, and the measurement accuracy is better than that of the Elman network. The Kalman filter is used to track and predict the micro gap weld, and the optimal estimation of the system state is obtained on the premise of the known weld position measurement information. Finally, the optimal prediction and estimation of the weld position are realized. After Kalman filter, the noise interference is greatly suppressed and the tracking accuracy is effectively improved.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TG441.7;TP391.41
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