Embedded vision system for monitoring arc welding with thermal imaging and deep learning

Fernandez, Andrea; Souto, Alvaro; González, Carlos Gómez; Méndez-Rial, Roi

doi:10.1109/coins49042.2020.9191650

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…Numerous applications have been successfully developed over the years within the field of infrared thermography research. In this regard, it has been applied in the field of material sciences (deformations and fractures, duct inspection, thermoplastic material inspection, welding process inspection, material deposition analysis, friction analysis of surfaces in contact, materials and structural mechanics heat transfer studies) [16], sports science [17], architecture and evaluation of buildings and constructions [18], historical and artistic heritage [19], inspection of electrical and electronic systems [20], aerial unmanned vehicles inspection [21], inspection and supervision of manufacturing processes [22,23], medical applications [24], security and surveillance [25], fire detection [26], etc.…”

Section: Infrared Thermographymentioning

confidence: 99%

Thermal Measurements Based on Image Processing for <i>In Situ</i> Monitoring of 3D Fused Filament Fabrication (FFF)

Cañero-Nieto,

Deluque Toro,

Solano Martos

et al. 2023

KEM

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Nowadays new applications based on the 3D printing technique demand increasingly strict product quality requirements. The in-situ monitoring of variables associated with the manufacturing process through the application of different techniques could help to evaluate the process and ultimately to ensure product quality. In this regard, the acquisition and evaluation of variables and indexes derived from thermographic analysis during the process are key for an early defect detection and can contribute to quality estimation. In this work, a new methodology is proposed for the monitoring and analysis of the additive manufacturing process based on the processing of thermographic images from an LWIR (Long Wave Infrared) camera. The methodology and the suitability of the variables and indexes extracted during the monitoring of the manufacturing process are discussed for the case of a 3D fused filament fabrication of polymers.

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Section: Infrared Thermographymentioning

confidence: 99%

Thermal Measurements Based on Image Processing for <i>In Situ</i> Monitoring of 3D Fused Filament Fabrication (FFF)

Cañero-Nieto,

Deluque Toro,

Solano Martos

et al. 2023

KEM

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“…IR thermography has been used for different kinds of applications, such as to monitor and control the weld geometry [31,32], to detect defects as lack of penetration and estimate of depth of penetration [33], and to perform seam tracking, bead width control, and cooling rate control to ensure acceptable weld quality with artificial neural networks [34]. The thermographic images have largely been studied with DL architectures [35][36][37], since they provide a gold standard for NDT methods. As an example, deep architectures have been employed to detect cracks [38] and characterize defects in a carbon-fiber-reinforced plastic specimen [39,40] by automatically analyzing thermal images and videos.…”

Section: Introductionmentioning

confidence: 99%

Inline Defective Laser Weld Identification by Processing Thermal Image Sequences with Machine and Deep Learning Techniques

et al. 2022

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The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neural network architecture to classify the malfunctioning weld joints with mean accuracy of 99% and median f1 score of 73% across five-fold cross validation on our locally acquired real world dataset. The outcome encourages the integration of thermographic-based quality control frameworks in all applications where fast and accurate recognition and safety assurance are crucial industrial requirements across the production line.

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“…However, in order to not add more weight to the robot, embedded systems able to acquire the image, preprocess it, and make inferences are required. In the context of Industry 4.0, priority is given to low-power, stand-alone, low-cost, multimodal, digitally robust, intercommunicable, and, above all, compact systems [6]. This paper focuses on the second precept, that is, of vision systems attached to a robotic arm.…”

Section: Introductionmentioning

confidence: 99%

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

et al. 2022

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The process of recognizing manufacturing parts in real time requires fast, accurate, small, and low-power-consumption sensors. Here, we describe a method to extract descriptors from several objects observed from a wide range of angles in a three-dimensional space. These descriptors define the dataset, which allows for the training and further validation of a convolutional neural network. The classification is implemented in reconfigurable hardware in an embedded system with an RGB sensor and the processing unit. The system achieved an accuracy of 96.67% and a speed 2.25× faster than the results reported for state-of-the-art solutions. Our proposal is 655 times faster than implementation on a PC. The presented embedded system meets the criteria of real-time video processing and it is suitable as an enhancement for the hand of a robotic arm in an intelligent manufacturing cell.

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Embedded vision system for monitoring arc welding with thermal imaging and deep learning

Cited by 13 publications

References 17 publications

Thermal Measurements Based on Image Processing for <i>In Situ</i> Monitoring of 3D Fused Filament Fabrication (FFF)

Thermal Measurements Based on Image Processing for <i>In Situ</i> Monitoring of 3D Fused Filament Fabrication (FFF)

Inline Defective Laser Weld Identification by Processing Thermal Image Sequences with Machine and Deep Learning Techniques

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

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