Quality detection of laser additive manufacturing process based on coaxial vision monitoring

Chen, Bin; Yao, Yongzhen; Huang, Yuhua; Wang, Wenkang; Tan, Caiwang; Feng, Jicai

doi:10.1108/sr-03-2018-0068

Cited by 12 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al [35] studied the influence of exposure time and filter on coaxial image quality, and found that the image of molten pool region obtained by 700nm filter and exposure time of 1μm was the clearest. Zhang et al [36] adopted a coaxial monitoring system to simultaneously monitor the state of molten pool and the crack of the welding joint, which overcame the interference of strong light and different radiation intensity in molten pool and keyhole during welding. Chen et al [37] used the coaxial visual monitoring system to capture the images of molten pool and established the relationship between the geometric size of molten pool and welding process parameters (as shown in Figure 8).…”

Section: Image Acquisition Methodsmentioning

confidence: 99%

The review of visual monitoring and recognition algorithm in laser welding

Wang

Shen

Zhang

et al. 2023

AOPC 2022: Advanced Laser Technology and Applications

View full text Add to dashboard Cite

There is abundant information in molten pool and keyhole during laser deep penetration welding. Their stability and characteristics are closely related to welding quality. At present, visual monitoring is an important means to obtain the characteristics of molten pool and keyhole. However, how to obtain a clear image, and how to optimize or develop an accurate and efficient algorithm are two major problems facing the visual monitoring of molten pool and keyhole. In view of the above problems, the paper combs the factors affecting molten pool visual monitoring and molten pool recognition algorithm. Firstly, the effects of camera type, auxiliary light source and filter, image acquisition method, the shielding gas and the air pressure on the image clarity of molten pool and keyhole are emphatically analyzed, and a method conducive to visual monitoring of molten pool and keyhole is proposed. Secondly, the influence of different algorithms and models on the monitoring accuracy of the system is analyzed. It is found that image pre-processing can highlight molten pool image information, and DBN (Deep Belief Networks) model has higher prediction accuracy through the processed molten pool image. Pointed out in the end of this article, the development of a laser welding monitoring device with clear image, stable and high dynamic response, the integrated development of multi-sensor synchronization visual monitoring device, and the development of laser welding process of adaptive fusion image processing algorithms will be the focus in the future, which provides a research direction for monitoring of molten pool and keyhole.

show abstract

Section: Image Acquisition Methodsmentioning

confidence: 99%

The review of visual monitoring and recognition algorithm in laser welding

Wang

Shen

Zhang

et al. 2023

AOPC 2022: Advanced Laser Technology and Applications

View full text Add to dashboard Cite

show abstract

“…A great deal of research is currently being carried out on real-time monitoring and process control of additive manufacturing forming processes. Chen B et al [ 8 ] used a CCD camera to capture images of the melt pool and explored the effect of different process parameters on the melt pool area. It was demonstrated that different types of defects could be accurately detected by analyzing the melt pool area.…”

Section: Introductionmentioning

confidence: 99%

Z-Increments Online Supervisory System Based on Machine Vision for Laser Solid Forming

Wang

et al. 2023

Micromachines

View full text Add to dashboard Cite

An improper Z-increment in laser solid forming can result in fluctuations in the off-focus amount during the manufacturing procedure, thereby exerting an influence on the precision and quality of the fabricated component. To solve this problem, this study proposes a closed-loop control system for a Z-increment based on machine vision monitoring. Real-time monitoring of the precise cladding height is accomplished by constructing a paraxial monitoring system, utilizing edge detection technology and an inverse perspective transformation model. This system enables the continuous assessment of the cladding height, which serves as a control signal for the regulation of the Z-increments in real-time. This ensures the maintenance of a constant off-focus amount throughout the manufacturing process. The experimental findings indicate that the proposed approach yields a maximum relative error of 1.664% in determining the cladding layer height, thereby enabling accurate detection of this parameter. Moreover, the real-time adjustment of the Z-increment quantities results in reduced standard deviations of individual cladding layer heights, and the height of the cladding layer increases. This proactive adjustment significantly enhances the stability of the manufacturing process and improves the utilization of powder material. This study can, therefore, provide effective guidance for process control and product optimization in laser solid forming.

show abstract

“…Zhang et al merged the images collected by dual cameras with 660 nm and 850 nm filters to obtain a clear molten pool image and predicted the weld width [35]. Chen et al used a coaxial CCD camera mounted on the welding head to shoot the molten pool, and studied the relationship between the molten pool area and laser power, defocus distance, welding speed and other process parameters through various image processing algorithms [36].…”

Section: Introductionmentioning

confidence: 99%

A New Method to Assess Fiber Laser Welding Quality of Stainless Steel 304 Based on Machine Vision and Hidden Markov Models

et al. 2020

View full text Add to dashboard Cite

High-power fiber laser welding has a broad range of applications in industrial processing and modern intelligent manufacturing, but how to assess the quality of laser welding has always been a concern. In the welding process, the geometric feature of the keyhole generated by the high temperature of laser can directly reflect quality of the laser welding. By using machine vision to acquire images in real time and analyze related features, the quality of laser welding can be evaluated, which reduce the cost and time in later inspection. Nevertheless, due to the dynamic complexity of the keyhole as well as the blurring of the keyhole and full penetration hole boundary caused by metal vapor and spatter, identification of multiple type defects through machine vision is still a problem that requires a solution. This paper presents a novel laser welding defect classification method using keyhole boundary-based feature based on machine vision and Hidden Markov process. After effective segmenting the collected welding image, the shapes of the keyhole as well as the full penetration hole were automatically extracted using the characteristics of gray projection distribution and the Poisson extinction method. Subsequently, a pre-trained Hidden Markov Model was employed to establish the connection between the keyhole's geometry and the welding quality defects. Experiments indicate that our theory can efficiently and accurately extract the key geometric shapes. Welding experimental data involving stainless steel 304 verifies the feasibility of the classification theory, which can monitor welding quality and reveal potential porosity and penetration defects.

show abstract

Quality detection of laser additive manufacturing process based on coaxial vision monitoring

Cited by 12 publications

References 18 publications

The review of visual monitoring and recognition algorithm in laser welding

The review of visual monitoring and recognition algorithm in laser welding

Z-Increments Online Supervisory System Based on Machine Vision for Laser Solid Forming

A New Method to Assess Fiber Laser Welding Quality of Stainless Steel 304 Based on Machine Vision and Hidden Markov Models

Contact Info

Product

Resources

About