Finite element optimization by pulsed thermography with adaptive response surfaces

Peeters, Jeroen; Steenackers, Gunther; Ribbens, Bart; Arroud, Galid; Dirckx, Joris J.J.

doi:10.21611/qirt.2014.039

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…The thermal contrast is an excellent technique to distinguish the temperature difference to learn the depth of the defects, as shown in Figure 1. [12] has become an important and economical platform to evaluate the thermal response of pulsed thermography, which builds up models for the platform of components that allow us to flexibly examine all specific physical aspects of thermal data such as geometries and properties of materials. Simulated thermograms matched well with experimental data.…”

Section: Pulsed Thermographymentioning

confidence: 99%

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Fang

Maldague

2020

Applied Sciences

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Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

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Section: Pulsed Thermographymentioning

confidence: 99%

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Fang

Maldague

2020

Applied Sciences

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“…Finite Element Modelling (FEM: COMSOL, etc.) [10] has become an important and economical platform to evaluate the thermal response of pulsed thermography, which builds up models for the platform of components that allow us to flexibly examine all specific physical aspects of thermal data such as geometries and properties of materials. Simulated thermograms matched well with experimental data.…”

Section: Finite Element Modeling With Transient Heat Transfermentioning

confidence: 99%

Defect Depth Estimation in Infrared Thermography with Deep Learning

Fang¹,

Maldague²

2020

Preprint

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Infrared thermography has already been proved to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Unites (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer (CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantify the depth of defects presented in the Plexiglasses materials. The proposed evaluated the accuracy and performance of synthetic plexiglasses data from FEM for defect depth predictions.

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“…The reflection can be ignored as Germanium has a reflection rate of less than 1% between a wave length of 7-12μm. In order not to have an influence of the wall behind the set-up, the bicycle frame will be placed at a distance of 1m from the wall, in order to avoid that the wall heats up due to the light bulb excitation [1,3]. For this reason, the wall reflection will not have an influence on the heat measurements.…”

Section: Fig 1 Experimental Setup For Ir Measurementsmentioning

confidence: 99%

“…On the other hand, computer simulation and virtual modelling tools are an economical and fast way to determine and simulate material behaviour. The next step in integrating the non-destructive PT tests and virtual modelling is the updating of the FE model [3], [4]. Computer simulations are economically interesting and a fast way to determine and simulate material behaviour and to calculate worst case scenarios.…”

Section: Introductionmentioning

confidence: 99%

From thermal inspection to updating a numerical model of a race bicycle

Steenackers¹,

Peeters²,

Ribbens³

et al. 2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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There are many advantages of using thermography for updating numerical models. These include the fact that it can be performed in-situ, it can cover large areas and it is a quantitative method. In this paper, a numerical model of a bicycle frame will be updated and optimized by the surface temperature distribution captured with pulsed thermography. These results will be compared and benchmarked against FRF measurement data as reference. The chosen temperature decay measurement to be used as reference data will be of key importance. Both updating techniques will be compared with respect to measurement time and accuracy.

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Finite element optimization by pulsed thermography with adaptive response surfaces

Cited by 9 publications

References 6 publications

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Defect Depth Estimation in Infrared Thermography with Deep Learning

From thermal inspection to updating a numerical model of a race bicycle

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