A Procedure for Detecting Hidden Surface Defects in a Thin Plate by Means of Active Thermography

Inglese, Gabriele; Olmi, Roberto

doi:10.1007/s10921-017-0440-6

Cited by 10 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The nondestructive structure testing technologies must be developed and improved from qualitative to quantitative approaches. 6,7 Combinations of intelligent optimization algorithms (e.g., genetic algorithms, artificial bee colony algorithms) and numerical methods have been widely applied for the quantitative detection of structural flaws. [8][9][10][11][12][13][14][15][16][17][18][19] Initially, the measured static responses (i.e., static displacement and strain) were used to construct the objective function in these studies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Jiang

Chen

Sun

et al. 2022

Numerical Meth Engineering

View full text Add to dashboard Cite

The identification of internal structural flaws is an important research topic in structural health monitoring. At present, structural safety inspections based on nondestructive testing procedures mainly focus on qualitative analysis; hence, it is difficult to identify the scale of flaws quantitatively. In this article, an inversion model that can realize quantitative detection is proposed by combing the scaled boundary finite element method (SBFEM) with deep learning. First, the lamb wave propagation processes in thin structures containing flaws were simulated using the SBFEM, and the echo wave signal at an observation point was recorded and paired with the flaw information. Then, the paired SBFEM datasets were used to train the deep learning model employing a convolutional neural network. A flaw classification and identification model based on SBFEM datasets was established. For a thin structure with unknown flaw information, and by using the measured observation point signals, the established deep learning model can predict flaw information in real time. Finally, the model performance was verified using several numerical examples. The results show that the proposed model can accurately predict flaw information and is robust against noise; thus, it offers an advantage over existing nondestructive testing procedures and a development in the field of structural health monitoring.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Following the development of certain technologies, engineers have afforded greater attention to the locations, sizes, and shapes of flaws. The nondestructive structure testing technologies must be developed and improved from qualitative to quantitative approaches 6,7 …”

Section: Introductionmentioning

confidence: 99%

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Jiang

Chen

Sun

et al. 2022

Numerical Meth Engineering

View full text Add to dashboard Cite

show abstract

“…The present work deals with A recent effective approach to the solution of this problem, based on Reciprocity Functional technique, is described in [4]. Here, a perturbative tool like Thin Plate Approximation (TPA)(see for example [5]) is used in alternative (see section 5). The idea of using TPA comes from the general assumption (see [3] Sect 9.2.1) that thermography is effective in detection of subsurface anomalies.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Thermal Conductance of a Hidden Barrier from Outer Thermal Data

Inglese¹,

Olmi

Scalbi

2021

The 16th International Workshop on Advanced Infrared Technology &Amp;amp; Applications

Self Cite

View full text Add to dashboard Cite

Hidden defects affecting the interface in a composite slab are evaluated from thermal data collected on the upper side of the specimen. First we restrict the problem to the upper component of the object. Then we investigate heat transfer through, the inaccessible interface by means of Thin Plate Approximation. Finally, a Fast Fourier Transform is used to filter data. In this way, we obtain a reliable reconstruction of simulated flaws in thermal contact conductance corresponding to appreciable defects of the interface.

show abstract

“…In non-destructive tests, the most used form of active thermography involving an external source of heat is flash thermography, also known as stepped thermography (PT). In PT, a heat step is applied to the surface of the sample and its thermal response is monitored during the cooling phase [9]- [11]. Different geometries, as well as internal discontinuities (e.g.…”

Section: Introductionmentioning

confidence: 99%

Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

et al. 2020

View full text Add to dashboard Cite

This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the physical principles of heat transfer. The results obtained from this algorithm are compared to the TSR technique and the raw thermogram obtained by stepped thermography inspection. Experimentally, a short thermal pulse is used and the surface temperature of the sample is monitored over time with an infrared camera. Due to the volume of data, the first step is data compression. Newton's law of cooling was used to store the normalized temperature data pixel-by-pixel over time and a compression ratio of 99% was obtained. The main contributions of the developed algorithm are: only four parameters for data compression and the concept of change in the direction of the heat flow to delimit the edges of the defects, where the borders are identified with a remarkable accuracy. Some well known image processing technique are also integrated to improve the thermal analysis: edge detection/interface between the sample and the image background; consolidation in a single image by aggregating the indicators referring to the concept of cooling/heating time constant, maximum thermal amplitude and contrast. INDEX TERMS active thermography, thermal analysis, infrared imaging, data compression, change detection algorithms, image processing.

show abstract

A Procedure for Detecting Hidden Surface Defects in a Thin Plate by Means of Active Thermography

Cited by 10 publications

References 20 publications

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Identification of the Thermal Conductance of a Hidden Barrier from Outer Thermal Data

Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

Contact Info

Product

Resources

About