EP4584584A2 - Contrôle thermographique de composants - Google Patents

Contrôle thermographique de composants

Info

Publication number
EP4584584A2
EP4584584A2 EP23786469.9A EP23786469A EP4584584A2 EP 4584584 A2 EP4584584 A2 EP 4584584A2 EP 23786469 A EP23786469 A EP 23786469A EP 4584584 A2 EP4584584 A2 EP 4584584A2
Authority
EP
European Patent Office
Prior art keywords
test object
test specimen
defect
detecting
infrared
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP23786469.9A
Other languages
German (de)
English (en)
Inventor
Gernot MAYR
Günther Mayr
Holger PLASSER
Gregor THUMMERER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Voidsy GmbH
Original Assignee
Voidsy GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Voidsy GmbH filed Critical Voidsy GmbH
Publication of EP4584584A2 publication Critical patent/EP4584584A2/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/72—Investigating presence of flaws
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/18—Investigating or analyzing materials by the use of thermal means by investigating thermal conductivity
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image

Definitions

  • the geometry detection system is designed to calculate a relative spatial position between the device and the test object.
  • the design of the device is also advantageous, according to which the evaluation device is designed for the program-controlled reconstruction of defects in a test specimen or of material differences or material properties of a test specimen, with the evaluation device generating a time- and location-dependent surface temperature signal Tmess from data from infrared images from the infrared detector array is calculated, and wherein the surface temperature signal Tmess is transformed into a mirror source representation Tsq using a regularization method.
  • This has the advantage that reconstructed component features can be assigned to the true component features (defects, interfaces, etc.) with greater reliability.
  • component identification is carried out by the device. This makes a priori information about the test subject available as additional information.
  • user authentication is carried out by the device. This makes the additional information dependent on the device user.
  • a contactless transmitter-receiver system radio-frequency identification technologies
  • optoelectronically detectable fonts bar codes or multi-dimensional codes (using a camera or scanner)
  • biometric authentication among others through face, Fingerprint, eye iris or voice recognition, manual authentication using a user interface or card reader, or digital authentication based on cryptographic handshake procedures
  • face Fingerprint
  • eye iris or voice recognition
  • manual authentication using a user interface or card reader or digital authentication based on cryptographic handshake procedures
  • an inertial measuring unit is arranged in the device, the infrared detector array of the surface scanners and the inertial measuring unit being arranged relative to one another at defined distances and defined orientations in the device.
  • the procedure in which the spatial level and orientation of the device relative to the test specimen is measured by the inertial measuring unit is also advantageous. This enables the image sequence to be corrected and stabilized using the inertial measuring unit.
  • external data sources are taken into account for detecting the spatial position and orientation of the device and for detecting the surface of the test object.
  • the extracted features of the defects can be used for data compression.
  • Fig. 1 shows a device for non-destructive component testing of a test specimen
  • Fig. 2 is a view of the device according to Fig. 1, corresponding to a viewing direction towards its sensors or towards its infrared detector array;
  • thermographic component testing shows a flowchart of the method for thermographic component testing
  • Fig. 4 details of the regularization process according to the “Regularization and Reconstruction” process step in Fig. 3; 5 shows the device when carrying out the thermographic test of the test specimen;
  • FIG. 6 shows an illustration of evaluation steps of the measured surface temperature signals carried out using the regularization method based on temporal and spatial temperature profiles
  • the device 1 shows a device 1 for non-destructive component testing of a component or test object 2.
  • the device 1 is suitable for carrying out a method for component testing using active thermography and includes an excitation source 3 and an infrared detector array 4.
  • an electrical Supply unit 5 is further provided with a control device 6 or a processor unit for controlling the test process and controlling or regulating the components of the device 1 necessary for this.
  • Part of the control device 6 is formed by an evaluation device 7 for program-controlled processing of the measurement signals detected by the infrared detector array 4 or by other sensors.
  • the device 1 comprises a surface scanner 8 for recording the geometric data or the geometric features of the test object 2.
  • the device 1 is also designed with an inertial measuring unit 9 for detecting the movement of the device 1.
  • FIG. 2 shows a view of the device 1 according to FIG. 1, corresponding to a viewing direction of the infrared detector array 4 or a viewing direction corresponding to an optical axis 12 of the infrared detector array 4.
  • the test specimen 2 has an inhomogeneity below a surface 13 in its interior - referred to in simple terms as a defect 14.
  • Fig. 3 shows, using a flow chart, the process of the thermographic component testing on the test object 2 (Fig. 1).
  • This is a multi-stage signal processing process.
  • the surface 13 of the test object 2 is first optically recorded and the initial pose (position and orientation) of the device 1 relative to the test object 2 or its surface 13 is determined.
  • the excitation source 3 one or more static (passive) infrared images of the test object 2 are captured with the infrared detector array 4.
  • the scanner data is consolidated (if necessary, removal of outliers, noise filtering, reduction of image size, reduction of local resolution). Furthermore, a normal vector n p is created for all relevant coordinate points on the surface 13 of the test object 2 certainly. The normal vectors n p are always oriented towards the interior of the component (Fig. 5).
  • the thermal response of the test object 2 is recorded for a sufficiently long time by recording infrared images with the infrared detector array 4.
  • the measurement duration and form of excitation determine the desired penetration depth into the test object 2.
  • the image repetition rate when the infrared images are recorded by the infrared detector array 4 determines the resolution in the axial direction of the device 1, that is, in the depth direction of the test object 2.
  • Section 101 includes measurements or detections that take place before the thermal excitation of the test object 2.
  • the thermal excitation takes place with the aid of the excitation source 3 and at the same time the thermal response is recorded by recording a sequence of infrared images by the infrared detector array 4.
  • the images recorded in the process by the infrared detector array 4 represent surface temperature data Tmess(p) from Points p on the surface 13 of the test specimen 2 as well as the time course of surface temperatures.
  • N u and N v describe the number of pixels of the area detector of the infrared detector array 4 in the u and v directions.
  • the number of time steps for the measurement is represented by Nt and the number of coordinate points in the depth direction is represented by Nw .
  • the transformation matrix KG IR A (Nt x N w ) can be represented by a Green's function with the thermal diffusivity oi33 in the following form.
  • Scaling parameter for optimizing the quality of the inverse solution and A t represents the temporal resolution of the surface temperature signal and A w represents the depth resolution for the mirror source representation.
  • the inverse problem is solved in the evaluation process using additional information, such as positivity or sparsity (solution matrix is sparse).
  • the calculated mirror source distribution as a function of depth reveals the characteristics of the measured surface temperature signal with respect to identical interfaces (surface 13) and defect interfaces in the form of sources (positive amplitude) and sinks (negative amplitude).
  • the sources and sinks can arise, for example, from defects or the prevailing boundary conditions, whereby features due to defects can be clearly distinguished from features due to the boundary conditions.
  • the boundary conditions or test environment characteristics can be described by thermal conduction, thermal radiation or convection or a mixture of these effects.
  • adiabatic boundary conditions only provide positive mirror source amplitudes.
  • Thermal radiation and convection can provide both negative and positive mirror source amplitudes (Figs. 6, 7). Based on the defect characteristics, the depth of the defect 14 can only be roughly estimated. In addition, due to the boundary conditions and the observation surface, the amplitudes do not provide any relevant information regarding the defect 14 and the position of the rear wall. Therefore, the amplitudes due to the defect 14 and the back wall are extracted. A noise-free defect temperature signal is then calculated from the resulting signal. By eliminating the noise signal, the depth of the defect can now be determined more precisely by evaluating the maximum defect temperature signal.
  • a replacement mirror source signal I 6 IR A (N U x N v x N w ) can then be determined for each pixel of the infrared detector array 4 and each defect signal for a multi-dimensional defect representation (see Fig. 6f and Fig. 6h).
  • the described transformation of the measured temperature signal into the corresponding mirror source representation does not take into account transverse diffusion processes or geometric information of the possibly anisotropic and complex shaped test specimen 2.
  • the surface temperature signals go through a sequence of several steps for their evaluation, regularization and defect reconstruction.
  • a first step 111 corresponds to the detection of the surface temperature signal Tme ss.
  • defect features are extracted.
  • the defect temperature signal is calculated based on the extracted defect features.
  • the following step 115 corresponds to determining the real defect depth from the calculated defect temperature signal.
  • the real defect depth is used in a subsequent step 116 to calculate a replacement mirror source signal.
  • a final step 117 local filtering takes place based on the measured geometric information of the test object 2 and information about the known thermal diffusivity tensor. Based on the results obtained, the size determination of the defect 14 in the test specimen 2 can be improved or optimized.
  • a relevant substitute feature P in the global coordinate system is composed of its position and its intensity.
  • the projection of replacement feature positions of one or more data sets I(u,v,w) into the global coordinate system leads to an aggregation of replacement features at or near the position of the physical component feature, provided that the replacement features belong to the same component feature (defect 14). .
  • This is the case, for example, with adjacent surface points from one measurement or congruent or adjacent surface points from several measurements (i.e. a multiple implementation of steps 101, 102 and 103 like. Fig. 3) from congruent or different spatial positions of the device 1.
  • thermographic measurements to be superimposed may differ from each other with regard to all common measurement parameters, since these can be taken into account as preliminary information when reconstructing and positioning the replacement features.
  • These measurement parameters can be: the temporal and spatial form of excitation by the excitation source 3; the measurement frequency (corresponding to the recording of thermal images by the infrared detector array 4); the spatial resolution and the distance of the surface 13 of the test object 2 or the spatial position of the device 1.
  • the depth resolution can be defined independently of the resolution of the geometry detection by the surface scanner 8.
  • the replacement feature intensity can also be assigned to a data point. This enables improved visualization of the features of the test specimen 2, for example by texturing point clouds or rendered surfaces of the features or defect 14. This also enables intensity values to be specified if the result data is in a three-dimensional, Cartesian grid with implicit coordinates ( Volume pixels, or “voxels” for short, should be present.
  • the replacement feature positions of relevant data points are transferred to the grid by interpolation.
  • thermographic reconstruction of the defect 14 will be explained in more detail below with reference to FIGS. 6 and 7.
  • FIG. 6 illustrates the evaluation procedure for reconstructing a defect 14 and for determining material parameters of the test specimen 2.
  • the illustration a) shows an example of a defective test specimen 2, where the thermal efficiency of the base material e2 is greater than the thermal efficiency of the defect material ei.
  • Diagram b) shows a characteristic temperature signal for a measurement in reflection mode in the defect area.
  • Diagram d) shows the mirror source distribution calculated from this.
  • Diagram c) shows the extracted defect and component features, diagram e) the defect temperature signal calculated from this and diagram f) the corrected depth distribution of the mirror sources.
  • Illustrations g) and h) show a 2D visualization of the extracted defect and component features and the corrected depth distribution of the mirror sources.
  • the method steps described correspond to the measures already presented in FIG. 4.
  • the expression of component features of the test specimen 2 depends on the thermal impedances of the base material and the defect material, the thermal impedances being formed by the respective effusivities, which in turn depend on the thermal conductivity k, the specific heat capacity c p and the material density p (Fig. 6a).
  • the greater the difference in thermal impedances the more clearly the characteristic component features of the test object 2 are displayed. Since the position of the image points in space is known, the individual feature intensities and positions can be used for more precise feature localization and quantification through appropriate overlay.
  • Component features under the surface 13 of the test specimen 2, which are imaged, for example, by a defect interface, can be due to their spatial expansion occurs in several adjacent pixels, whereby at the same time a distortion of the spatial expansion occurs due to heat diffusion.
  • 6b shows an example of a measured surface temperature signal as a function of time for the reflection (pulse-echo) configuration (excitation source 3 and infrared detector array 4 on the same side) based on a very short excitation pulse in the form of a Dirac delta distribution regarding time.
  • thermographic defect reconstruction is applicable to all temporal and local thermal excitation functions, as well as in the case of a transmission configuration (excitation source 3 and infrared detector array 4 on opposite sides).
  • a test measurement can be carried out. With a test measurement, the thermal diffusion time td and thus the measurement time tme ss can be determined.
  • the temperature signal measured after thermal excitation can be transformed locally, i.e. pixel by pixel, into a corresponding mirror source representation.
  • the diagram, shown in Fig. 6d illustrates the calculated mirror source distribution as a function of depth and reveals the characteristics of the measured surface temperature signal related to component interfaces and defect interfaces in the form of sources (detected by positive amplitudes) and sinks (detected by negative amplitudes). By extracting the amplitudes due to the defect 14 and the back wall, the diagram as shown in Fig. 6c is finally obtained.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Investigating Or Analyzing Materials Using Thermal Means (AREA)
  • Radiation Pyrometers (AREA)

Abstract

L'invention concerne un dispositif et un procédé de contrôle thermographique de composants, faisant appel à une source d'excitation pour générer un flux thermique non stationnaire dans une pièce à contrôler, un réseau de détecteurs infrarouges pour détecter un rayonnement thermique émis par une surface de la pièce à contrôler, un dispositif de balayage de surface, un dispositif de commande et un dispositif d'évaluation, le dispositif comprenant une unité de mesure inertielle pour détecter des mouvements du dispositif.
EP23786469.9A 2022-09-07 2023-09-06 Contrôle thermographique de composants Pending EP4584584A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ATA50679/2022A AT526462B1 (de) 2022-09-07 2022-09-07 Thermographische Bauteilprüfung
PCT/AT2023/060312 WO2024050586A2 (fr) 2022-09-07 2023-09-06 Contrôle thermographique de composants

Publications (1)

Publication Number Publication Date
EP4584584A2 true EP4584584A2 (fr) 2025-07-16

Family

ID=88315894

Family Applications (1)

Application Number Title Priority Date Filing Date
EP23786469.9A Pending EP4584584A2 (fr) 2022-09-07 2023-09-06 Contrôle thermographique de composants

Country Status (7)

Country Link
US (1) US20260086057A1 (fr)
EP (1) EP4584584A2 (fr)
JP (1) JP2025539675A (fr)
CN (1) CN119998653A (fr)
AT (1) AT526462B1 (fr)
DE (1) DE202023003150U1 (fr)
WO (1) WO2024050586A2 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN121559628A (zh) * 2026-01-22 2026-02-24 北京军安中科信息科技研究所 一种红外成像式非线性节点探测器

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US3433052A (en) * 1965-07-26 1969-03-18 Automation Ind Inc Material tester
JP3940591B2 (ja) * 2001-11-28 2007-07-04 沖電気工業株式会社 半導体装置の電気特性のシミュレーション方法
CA2951220C (fr) * 2014-06-30 2019-02-26 Bodidata, Inc. Systeme multicapteur portatif permettant de definir la taille d'objets irreguliers
US9519844B1 (en) * 2016-01-22 2016-12-13 The Boeing Company Infrared thermographic methods for wrinkle characterization in composite structures
DE102016212810B4 (de) * 2016-07-13 2023-04-27 Audi Ag Verfahren und Messsystem für die Prüfung eines Bauteils durch aktive Thermografie
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Non-Patent Citations (1)

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Title
HOLLAND S D ET AL: "Model-based Inversion for Pulse Thermography", EXPERIMENTAL MECHANICS, SPRINGER US, NEW YORK, vol. 59, no. 4, 3 January 2019 (2019-01-03), pages 413 - 426, XP036794435, ISSN: 0014-4851, [retrieved on 20190103], DOI: 10.1007/S11340-018-00463-2 *

Also Published As

Publication number Publication date
CN119998653A (zh) 2025-05-13
DE202023003150U1 (de) 2026-04-30
AT526462A1 (de) 2024-03-15
WO2024050586A3 (fr) 2024-05-02
WO2024050586A2 (fr) 2024-03-14
AT526462B1 (de) 2025-02-15
JP2025539675A (ja) 2025-12-08
AT526462A9 (de) 2024-05-15
US20260086057A1 (en) 2026-03-26

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