首页|基于改进CeiT的GTAW焊接熔透状态识别方法

基于改进CeiT的GTAW焊接熔透状态识别方法

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针对熔池信息与背景相似度高、噪声多、预测实时性差、识别精度低等问题,提出了基于改进CeiT网络模型的GTAW焊接熔透状态识别方法.首先通过MobileNetV3对Image-to-Tokens模块进行轻量化改进,提升预测的实时性能;其次设计DGCA模块改进LeFF模块来增强特征间的远程依赖关系、丰富类标记中所包含的分类信息;最后将LeFF模块中的底层特征和高层语义特征进行融合,提高模型对熔池特征的表示能力.仿真结果表明,与MobileNetV3,ResNet50,ShuffleNetV2,DeiT和CeiT模型相比,所提出的模型获得了更高的准确率和较快的检测速度.
Identification method of GTAW welding penetration state based on improved CeiT
Aiming at the problems of high similarity between melt pool information and background,much noise,poor real-time prediction and low recognition accuracy,a GTAW weld-ing fusion state recognition method based on improved CeiT network model is proposed.First,the Image-to-Tokens mod-ule is lightened and improved by MobileNetV3 to enhance the real-time performance of prediction;second,the DGCA mod-ule is designed to improve the LeFF module to enhance the re-mote dependencies among features and enrich the categorical information contained in the class labels;and lastly,the fusion of the underlying features and the high-level semantic features in the LeFF module improves the model's ability to represent the features of the melt pool.Simulation experiments show that the proposed model obtains higher accuracy and faster de-tection speed compared with MobileNetV3,ResNet50,Shuffle-NetV2,DeiT,and CeiT models.

image processingmelt poolpenetration stateCeiT networkgas tungsten arc welding

王颖、高胜

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东北石油大学,大庆,163318

图像处理 熔池 熔透状态 CeiT网络 熔化极气体保护焊

国家自然科学基金国家重点研发计划黑龙江省自然科学基金黑龙江省博士后专项东北石油大学青年科学基金

617020932018YFE0196000F2018003LBH-Q200772020QNL-10

2024

焊接学报
中国机械工程学会 中国机械工程学会焊接学会 机械科学研究院哈尔滨焊接研究所

焊接学报

CSTPCD北大核心
影响因子:0.815
ISSN:0253-360X
年,卷(期):2024.45(4)
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