Process stability analysis and weld formation evaluation during disk laser–mag hybrid welding

Gao, Xiangdong; Wang, Lin; Chen, Ziqin; Zhang, Yanxi; You, Deyong

doi:10.1016/j.optlaseng.2019.105835

Cited by 19 publications

(11 citation statements)

References 31 publications

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“…Hence, the classifcation task is completed accurately at a faster rate. Te output images of the Proposed method are shown in Figure 3 with the numbering from [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Image segmentation is generally used to identify objects and boundaries (lines, curves, etc.)…”

Section: Resultsmentioning

confidence: 99%

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Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Jacintha

Karthikeyan

Sivaprakasam

2023

Journal of Engineering

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This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The thermal images detect the surface flaws and never subsurface flaws in Plug valves. The subsurface flaws detection is a challenging problem in valve inspection. In this paper, the thermal images obtained after the dye penetrates the surface valve detect the surface flaws more efficiently after applying the Fuzzy Deep Learning Algorithms. DyePenetrating Test (DPT) combined with Infrared Thermography is proposed to identify heat flux changes and flaws in the faulty metal surface of Plug valves. In DPT, thinned paint is employed on the metal surface that displays metal porosity and even fine cracks. After DPT, thermal images of the Plug valve are processed through the Fuzzy Deep Learning Algorithm to evaluate flaws. The Fuzzy Algorithm is utilized prior to Deep Learning to simplify and speed up the classification task. The flaws are identified using Slicing operation and the following parametric quantities such as Accuracy, Mathew’s Correlation Coefficient (MCC), Local Self-Similarity Descriptor (LSS), Precision/Recall, F-measure, and Jaccard Index. The parametric quantities depict corresponding variations with regard to surface coarseness and metal flaws. The DPT and Fuzzy Deep Learning Algorithm identify metal defects with 80.67% accuracy.

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Section: Resultsmentioning

confidence: 99%

“…Gao et al [11] Process stability and weld formation (laser welding) Analysis with a high-speed camera.…”

Section: Coaxial Infrared Pyrometermentioning

confidence: 99%

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Jacintha

Karthikeyan

Sivaprakasam

2023

Journal of Engineering

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“…The training phase took place in the cloud, through the use of the GPUs offered by the Google Colab service. 5 Note that the hyper-parameters defined so far are the result of a try and error phase, where their combination provided the best result on the validation set.…”

Section: Methodsmentioning

confidence: 99%

“…Another example of process monitoring is that offered by [5]: they collect the process information of a hybrid laser-MAG (Metal Active Gas) welding system with two cameras at high frame per second rate and extract the information through a convolution with Gabor filter. A monitoring system for a robotic arc welding process is also proposed by [23].…”

Section: Related Workmentioning

confidence: 99%

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

Martinelli

Gamberini²,

Lippi³

2021

Preprint

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In this paper we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The product operates as an interface between an input torque, typically supplied by an agricultural vehicle, and an output torque, generally moving specific equipment placed on a trolley equipped with a tow hook. The "as-is'' (initial version) line was highly inefficient due to several critical issues, including the high age of the machines, a non-optimal arrangement of them in the shop floor, and the absence of controls and process standards. These critical issues were analysed with the tools offered by Lean Manufacturing, which made it possible to identify irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the "to-be'' (new version) assembly line included actions to update the department layout, to modify the assembly process and to design the line feeding system in compliance with the well-known concepts of Golden Zone and Strike Zone. The whole process addressed, in particular, the problem of the incorrect assembly of the oil seals. The registered error was mainly caused by the difficulty in visually identifying the correct side of the assembled oil seal, and by the mental fatigue of operators at the end of the shift. The solution studied in this paper resulted in a Poka-Yoke solution, which, leveraging the modern technologies and methods of deep learning and computer vision, monitors the process flow of the operators through a camera, preventing its completion in the event of an assembly error.

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“…[25][26][27]. To illustrates this, researchers studied various imaging methods and image processing methods [1,[28][29][30][31][32][33]. Katayama et al studied the reflected laser beam and radiated light in the laser welding process of aluminum alloy as the monitoring signal, and clarified the correlation between the monitoring signal and the welding phenomenon when the welding defect was formed [30].…”

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

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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.

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Process stability analysis and weld formation evaluation during disk laser–mag hybrid welding

Cited by 19 publications

References 31 publications

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

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

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