本文選題：磁光成像 + 焊接缺陷��； 參考：《光學(xué)精密工程》2016年04期

【摘要】：針對焊縫微小凹陷、未熔合和焊偏等焊接缺陷,提出了基于磁光成像無損探傷的小波多尺度邊緣提取算法及主成分分析-誤差反向傳播神經(jīng)網(wǎng)絡(luò)(PCA-BP)缺陷分類模型;研究了焊件表面及近表面缺陷的可視化無損檢測及分類方法。首先,通過對焊件施加感應(yīng)磁場,利用法拉第磁致旋光原理構(gòu)成磁光傳感器,獲取焊接缺陷磁光圖像。然后,針對焊接缺陷磁光圖像存在噪聲干擾、對比度低且成像背景復(fù)雜等特征,基于小波模極大值的多尺度邊緣信息融合方法,設(shè)計(jì)了具有高抗噪性的缺陷邊緣檢測算法。最后,通過PCA法對磁光圖像列方向灰度變量進(jìn)行預(yù)處理,得到能表征95%磁光圖像列方向灰度變量信息的256個(gè)特征點(diǎn)作為輸入特征量,構(gòu)建了三層BP神經(jīng)網(wǎng)絡(luò)模型,對焊接缺陷樣本進(jìn)行分類。試驗(yàn)結(jié)果表明,所提方法能準(zhǔn)確識別微小凹陷、未熔合和焊偏等焊接缺陷,模型分類準(zhǔn)確率可達(dá)90.80%。
[Abstract]:Aiming at welding defects such as micro-depression, non-fusion and welding deviation, a wavelet multi-scale edge detection algorithm based on magneto-optic imaging nondestructive testing and a PCA-BP-based defect classification model based on principal component analysis (PCA) and error back-propagation neural network (PCA-BP) are proposed.Visual nondestructive testing and classification method for surface and near surface defects of welded parts are studied.Firstly, the magneto-optic sensor is constructed by applying magnetic field and Faraday magneto-optic principle to obtain the magneto-optic image of welding defect.Then, aiming at the characteristics of noise interference, low contrast and complex imaging background in the magneto-optical image of welding defects, a high noise resistant defect edge detection algorithm is designed based on wavelet modulus Maxima based multi-scale edge information fusion method.Finally, a three-layer BP neural network model is constructed by pre-processing the grayscale variables in the column direction of magneto-optic images by PCA method, and obtaining 256 feature points which can represent the information of grayscale variables in the column direction of 95% magneto-optic images.The welding defect samples are classified.The experimental results show that the proposed method can accurately identify the welding defects such as micro-depression, non-fusion and welding deviation, and the accuracy of model classification can reach 90.80.
【作者單位】： 廣東工業(yè)大學(xué)機(jī)電工程學(xué)院廣東省計(jì)算機(jī)集成制造重點(diǎn)實(shí)驗(yàn)室;廣州番禺高勛染整設(shè)備制造有限公司;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(No.51175095) 廣東省協(xié)同創(chuàng)新與平臺環(huán)境建設(shè)專項(xiàng)基金資助項(xiàng)目(No.2015B090901013) 廣東省重大科技專項(xiàng)資助項(xiàng)目(No.2014B090921008) 廣州市科學(xué)研究專項(xiàng)基金資助項(xiàng)目(No.201510010089) 佛山市科技創(chuàng)新專項(xiàng)基金資助項(xiàng)目(No.2014AG10015)
【分類號】：TP391.41

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