Titlebook: Key Technologies of Intelligentized Welding Manufacturing; Visual Sensing of We Zongyao Chen,Zhili Feng,Jian Chen Book 2021 Springer Nature

倔強(qiáng)一點(diǎn)

Penetration Prediction with Machine Learning Models, usually requires a sensor on the back of the weldment which is not practical in many welding applications. In the manual welding process, the geometry of weld pool top surface geometry is a major reference for welder to determine the penetration condition. This chapter focuses on establishing an ar

飛鏢

Penetration Detection of Narrow U-Groove Welding,ation for the butt joint weld. By filling a groove design joint makes it possible to weld the workpieces with less heat input. The root pass weld is the first weld performed on the root of the groove joint. To perform a high-quality weld, the first pass of the weld should fully penetrate the root fa

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https://doi.org/10.1007/978-981-15-6491-8Intelligent Welding; Robotic Welding; Optical Sensing; Machine Learning; Adaptive Control; Feature Extrac

眼界

Zongyao Chen,Zhili Feng,Jian ChenProvides a comprehensive overview of non-destructive methods for detecting and reducing welding defects during gas tungsten arc welding (GTAW) based on computer vision and artificial intelligence.Pres

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978-981-15-6493-2Springer Nature Singapore Pte Ltd. 2021

節(jié)約

不安

Conclusions and Recommendations,ld pool geometry is the major source of information related to the formation of these defects. In this dissertation, a new visual sensing and control system was developed to eliminate the welding defects during GTAW.

咯咯笑

Monitoring Weld Pool Surface with Active Vision Image,In this chapter, a new weld pool monitoring system was established with laser light illumination, allowing us to observe the dynamic weld pool geometry during welding. The weld pool dynamic behavior under different welding currents and speed ranges was studied.