Improving process reliability by means of detection of weld seam irregularities in copper via thermographic process monitoring

Schaumberger, Kerstin; Beck, Markus; Saffer, J.; Kaufmann, Florian; Ermer, Jakob; Roth, Stephan; Schmidt, Michael

doi:10.1016/j.promfg.2019.08.009

Cited by 7 publications

(6 citation statements)

References 7 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%

“…Te Fuzzy Clustering outputs are denoted in Figure 5 with the numbering from [1][2][3][4][5][6][7][8][9][10][11][12][13][14] and Fuzzy Slicing outputs are denoted in Figure 6 with the numbering from [5,[15][16][17][18][19][20][21]. Using fuzzy, it processes the image and forms clusters, totally…”

Section: Resultsmentioning

confidence: 99%

“…Schaunberger et al [10] To identify weld seam defects such as pores, tapers, and regressions (copper) Defect detected from temperature curves.…”

Section: Coaxial Infrared Pyrometermentioning

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%

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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“…In the research field of laser process monitoring, a wide range of optical sensor approaches [2][3][4] were investigated to enable inline monitoring and a closed-loop control of the process [5]. Within the referenced scientific studies, correlations between the evaluated sensor signals and the welding depth were examined.…”

Section: Inline Weld Depth Measurement For Deep Penetration Laser Bea...mentioning

confidence: 99%

Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control

et al. 2022

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In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now.

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“…When higher spatial resolution is required, CCD (charged-coupled device) or CMOS (complementary metaloxide-semiconductor) cameras [27] are widely adopted. The use of thermal [28] and hyperspectral [29] cameras has also been reported to estimate the weld pool temperatures. These devices offer the possibility to have a comprehension of the process through images instead of simple signals.…”

Section: Introductionmentioning

confidence: 99%

Fiber laser welding of AA 5754 in the double lap-joint configuration: process development, mechanical characterization, and monitoring

Garavaglia

Demir

Zarini³

et al. 2020

Int J Adv Manuf Technol

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Aluminum alloys are widely spread in many industrial sectors due to their desirable characteristics as low density, good formability, high specific strength, and good resistance to corrosion. Autogenous laser welding is a technology that enables the use of these materials in the industrial process due to its high repeatability, reliability, and ease of automatization. In particular, in automotive applications, Al-alloys are welded in lap-joint configurations with more than 2 layers of material. The welding condition should be monitored in order to detect the complete penetration, hence guaranteeing the appropriate weld resistance. The use of non-invasive and coaxial monitoring solutions is highly desirable for the identification of weld defects during the process. This study investigates an autogenous laser welding process and monitoring in the double lap-joint configuration of sheets of AA 5754. First, the process parameters are investigated to identify the geometrical and mechanical characteristics of the resultant welding seams at different process conditions. The employed high-brilliance 3 kW fiber laser provided the possibility of reading the back-reflected light signal from an internal photodiode. The capability of this signal to be used as a non-invasive, coaxial, and remote monitoring system in order to predict the process outcome was tested. In the experiments the back-reflected light intensity could be correlated to the weld seam width at the second interface, as well as the strength of the joint to shear. Finally, the monitoring signal behavior was demonstrated under simulated weld defect conditions. The results show that weld anomalies such as lack of penetration, misalignment, and gap formation can be sensed through the monitoring approach.

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Improving process reliability by means of detection of weld seam irregularities in copper via thermographic process monitoring

Cited by 7 publications

References 7 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

Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control

Fiber laser welding of AA 5754 in the double lap-joint configuration: process development, mechanical characterization, and monitoring

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