scholarly journals Power Control during Remote Laser Welding Using a Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6658
Author(s):  
Alex Božič ◽  
Matjaž Kos ◽  
Matija Jezeršek

The increase in complex workpieces with changing geometries demands advanced control algorithms in order to achieve stable welding regimes. Usually, many experiments are required to identify and confirm the correct welding parameters. We present a method for controlling laser power in a remote laser welding system with a convolutional neural network (CNN) via a PID controller, based on optical triangulation feedback. AISI 304 metal sheets with a cumulative thickness of 1.5 mm were used. A total accuracy of 94% was achieved for CNN models on the test datasets. The rise time of the controller to achieve full penetration was less than 1.0 s from the start of welding. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was used to further understand the decision making of the model. It was determined that the CNN focuses mainly on the area of the interaction zone and can act accordingly if this interaction zone changes in size. Based on additional testing, we proposed improvements to increase overall controller performance and response time by implementing a feed-forward approach at the beginning of welding.

Author(s):  
Zhehao Zhang ◽  
Yi Zhang ◽  
Feng Luo ◽  
Jie Li ◽  
Cheng Lu ◽  
...  

Abstract Convolutional neural network (CNN) is an efficient and robust method which can accurately detect the Tailor Rolled Blank laser welding pool penetration status. To select proper hyperparameters and optimization of CNN model are black box problem. In this paper, an innovative method based on CNN to identify the penetration status of the weld pool during laser welding was introduced. A coaxial monitoring platform is set up, as well as two-class, three-class and four-class datasets are created for training and validating the CNN. The Bayesian Optimization (BO) method is used to optimize hyper-parameters which are adopted for training CNN model, determine the best parameters of depth, initial learning rate, momentum and L2 regularization. The results show that using BO method leads to accuracy improvement compared with the CNN model trained from scratch with default hyper-parameters, hence it can effectively solve the problem that the hyper-parameters of CNN are difficult to adjust. Under various laser welding parameters, high-accuracy detection of penetration status can be acquired with the test accuracy of four-class reaching 95.2%, which slightly lower than the test accuracy of the three-class and two-class.


2020 ◽  
Vol 54 ◽  
pp. 348-360 ◽  
Author(s):  
Zhehao Zhang ◽  
Bin Li ◽  
Weifeng Zhang ◽  
Rundong Lu ◽  
Satoshi Wada ◽  
...  

2016 ◽  
Vol 61 (1) ◽  
pp. 93-102 ◽  
Author(s):  
A. Lisiecki

The paper presents a detailed analysis of the influence of heat input during laser bead-on-plate welding of 5.0 mm thick plates of S700MC steel by modern Disk laser on the mechanism of steel penetration, shape and depth of penetration, and also on tendency to weld porosity formation. Based on the investigations performed in a wide range of laser welding parameters the relationship between laser power and welding speed, thus heat input, required for full penetration was determined. Additionally the relationship between the laser welding parameters and weld quality was determined.


2012 ◽  
Vol 496 ◽  
pp. 272-275
Author(s):  
Jian Tian ◽  
Zhong Ning Zhang

There are the four types of defects during remote laser welding of zinc coated sheet metal. The root cause of all these defects is the explosion or ejection of molten weld metal caused by the escape of trapped high pressurized zinc vapor. Zinc removal is one of the methods used to solve the defects. We have researched the full penetration zinc removal method for remote laser welding with coupons of zinc coated sheet metal. The result shows that the full penetration zinc removal method works at the cost of high heat input and low welding speed


2021 ◽  
Vol 1884 (1) ◽  
pp. 012008
Author(s):  
Deyuan Ma ◽  
LeShi Shu ◽  
Qi Zhou ◽  
Shenjie Cao ◽  
Ping Jiang

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