An Automotive Body-in-White Welding Stud Flexible and Efficient Recognition System

被引：0

作者：

Huang, Hong ^{[1
]}

Peng, Xiangqian ^{[1
]}

Wu, Shiyu ^{[2
]}

Ou, Wenchu ^{[1
]}

Hu, Xiaoping ^{[3
]}

Chen, Lifeng ^{[1
]}

机构：

[1] Hunan Univ Sci & Technol, Sch Mech Engn, Xiangtan 411201, Peoples R China

[2] SA Motor Res & Dev Innovat Headquarters, Shanghai 201805, Peoples R China

[3] Hunan Prov Key Lab Hlth Maintenance Mech Equipment, Xiangtan 411201, Peoples R China

来源：

IEEE ACCESS | 2025年 / 13卷

基金：

中国国家自然科学基金;

关键词：

Welding; Automobiles; Accuracy; Position measurement; Inspection; Deep learning; Automotive engineering; Feature extraction; Analytical models; Fasteners; Automobile industry; welded studs; deep learning; target detection; flexible detection system;

D O I：

10.1109/ACCESS.2025.3553691

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The traditional stud welded inspection method suffers from low efficiency and a lack of precision, which makes it difficult to meet the demand for high efficiency and precision in modern automobile production lines. In this paper, a flexible recognition system incorporating a deep learning model is proposed for the inspection of welded studs in the whole car body-in-white of an automobile, including the composition of the recognition system, error analysis, path planning, and the deep learning model used. The proposed system has made significant improvements to both accuracy and detection efficiency. It can detect welded studs in a white car body with high accuracy and efficiency. Its overall detection accuracy of 99.36% demonstrates its potential to automate the production line and reduce production costs.

引用

页码：51938 / 51955

页数：18

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