基于特征提取的粉末床缺陷识别分类方法  

Defect Identification and Classification Method for Powder Bed Based on Defect Feature Extraction

作　　者：冯景威 邢飞 卞宏友 苗立国 Feng Jingwei;Xing Fei;Bian Hongyou;Miao Liguo(College of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,Liaoning,China;Nanjing Zhongke Yuchen Laser Technology Co.,Ltd.,Nanjing 210038,Jiangsu,China)

机构地区：[1]沈阳工业大学机械工程学院,辽宁沈阳110870 [2]南京中科煜宸激光技术有限公司,江苏南京210038

出　　处：《应用激光》2025年第1期26-35,共10页Applied Laser

基　　金：国家科技重大专项(2019-Ⅶ-0004-0144);南京市科技计划项目(202208019)。

摘　　要：针对激光选区熔化设备的3类粉末床缺陷,提出一种基于特征提取的视觉检测方法。对于光源照射造成的亮度不均匀现象,设计自适应亮度校正算法消除光照对图像的影响。将图像预处理得到缺陷轮廓,依据图像的灰度信息区分缺陷类型进行特征提取。条纹缺陷采用Hough变换识别,对另两类缺陷,提取方向梯度直方图(HOG)特征、纹理特征和形状特征,将特征向量降维,输入AdaBoost集成学习算法进行训练,得到分类模型。实验结果表明,该算法可有效区分3类粉末床缺陷,识别准确率为97.29%,平均检测时间小于500 ms,可实现快速准确识别。A visual detection method based on feature extraction is proposed for three types of powder bed defects in laser selective melting equipment.An adaptive brightness correction algorithm is designed to eliminate the influence of lighting on images caused by uneven brightness from the light source.The defect contours are obtained through image preprocessing,and based on the grayscale information of the images,the defect types are differentiated for feature extraction.Stripe defects are identified using the Hough transform.For the other two types of defects,direction gradient histograms(HOG)features,texture features,and shape features are extracted.The feature vectors are then dimensionally reduced and inputted into the AdaBoost ensemble learning algorithm for training to obtain the classification model.Experimental results show that this algorithm can effectively distinguish the three types of powder bed defects with an identification accuracy of 97.29%.The average detection time is less than 500 ms,enabling fast and accurate recognition.

关 键 词：激光选区熔化 特征提取 亮度校正 方向梯度直方图 集成学习 

分 类 号：TG249.5[机械工程—精密仪器及机械] TP391[金属学及工艺—铸造]