Crack-Depth Prediction in Steel Based on Cooling Rate

Rodríguez‐Martín, Manuel; Lagüela, S.; González‐Aguilera, Diego; Rodríguez‐Gonzálvez, Pablo

doi:10.1155/2016/1016482

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Currently, some authors are investigating other, more sophisticated techniques for the detection and identification of external pathologies in welds: laser systems [10,11], scanning cameras [12], hybrid laser-based systems and cameras [13][14][15][16], stereo imagery [12,17], closerange photogrammetry [9,18], and, finally, active thermography systems as supports for visual inspection, which are applied directly to the study of cracks in welds [19].…”

Section: Introductionmentioning

confidence: 99%

WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds

et al. 2022

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This paper presents a new tool for external quality control in welds using close-range photogrammetry. The main contribution of the developed approach is the automatic assessment of welds based on 3D photogrammetric models, enabling objective and accurate analyses through an in-house tool that was developed, WELDMAP. As a result, inspectors can perform external quality control of welds in a simple and efficient way without requiring visual inspections or external tools, and thus avoiding the subjectivity and imprecisions of the classical protocol. The tool was validated with a large dataset in laboratory tests as well as in real scenarios.

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Section: Introductionmentioning

confidence: 99%

WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds

et al. 2022

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“…Physical properties, such as the temperature [ 5 ] or heating-cooling rate obtained from AT [ 6 ], can be used as predictive parameters whene stimating the depth of cracks in steel. In fact, in Rodríguez-Martin et al [ 6 ], a pixelwise algorithm for time derivative of temperature (PATDT) was developed to predict geometric features of the crack in steel welds from a sequence of thermograms using AT.…”

Section: Introductionmentioning

confidence: 99%

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Rodríguez‐Martín

Fueyo

Aguilera

et al. 2020

Sensors

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The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

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“…Therefore, it is difficult to visually inspect each of them. To solve these problems, a number of thermographic data processing methods have been proposed, such as principal component thermography (PCT) [1], pulsed phase thermography (PPT) [2], thermographic signal reconstruction (TSR) [3], higher-order statistics (HOS) [4], pixelwise algorithm for time-derivative of temperature (PATDT) [5,6],and so on. However, the concept of blind source separation (BSS) has seldom been adopted.…”

Section: Introductionmentioning

confidence: 99%

Independent Component Thermography for Subsurface Defect Detection

Wu¹,

Сфарра²,

Wen³

et al. 2018

Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography

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In this work, the concept of blind source separation (BSS) is introduced into the field of thermographic data processing to facilitate subsurface defect detection. In detail, the thermal images generated by infrared thermography are viewed as a set of mixed signals of multiple independent and non-Gaussian sources, which can be decomposed by using the proposed independent component thermography (ICT) technique. The decomposition results highlight the locations and shapes of the defective regions by eliminating the effects of backgrounds. The feasibility of ICT is illustrated with its application to the analysis of the conservation status of a panel painting.

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Crack-Depth Prediction in Steel Based on Cooling Rate

Cited by 8 publications

References 18 publications

WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds

WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

Independent Component Thermography for Subsurface Defect Detection

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