Thermographical component testing

Abstract

The invention relates to a device and a method for thermographically testing a component, comprising an excitation source for generating a transient heat flow in a test sample, an infrared detector array for detecting thermal radiation emitted from a surface of the test sample, a surface scanner, a control device and an evaluation device, wherein the device comprises an inertial measuring unit for detecting movements of the device.

Description

[0001] THERMAL GRAPHIC COMPONENT TESTING

[0002] The invention relates to a device and a method for non-destructive component testing.

[0003] Active thermography is a state-of-the-art non-destructive imaging testing method for material and component characterization. It is based on the thermal excitation of the test specimen by absorbing optical radiation, inducing eddy currents, coupling mechanical waves, or other forms of energy that lead to a time-dependent temperature change in the test specimen. Infrared sensors—designed as point, line, or area detectors—can detect the thermal radiation of the test specimen without contact. Based on the measured temperature field on the component surface, the time-varying heat diffusion process is analyzed, and component characteristics can be detected and identified. Known technical implementations of active thermography include laboratory setups on fixed stands or mobile, hand-held, or robot-guided testing systems.

[0004] The object of the present invention is to provide a device and a method for non-destructive component testing with which deep-seated component features or defects can be assigned to the three-dimensional component geometry with greater reliability. Furthermore, it is an object of the invention to provide a device with which non-destructive component testing can be used in a wider range of applications and with improved user-friendliness.

[0005] This object is achieved by a device and a method according to the claims.

[0006] The device according to the invention for thermographic component testing comprises an excitation source for generating a transient heat flow in a test piece, an infrared detector array for detecting thermal radiation emitted from a surface of the test piece, a surface scanner, a control device, and an evaluation device. The device comprises an inertial measuring unit for detecting movements of this device. Furthermore, the device comprises a physical unit for user authentication. Of particular advantage is the development of the device according to which the evaluation device comprises a geometry detection system for calculating spatial coordinates of the surface of the test piece.

[0007] According to an advantageous embodiment of the device, it is provided that the geometry detection system is designed to calculate a relative spatial position between the device and the test object.

[0008] A further development of the device provides for the infrared detector array of the surface scanner and the inertial measurement unit to be arranged relative to each other within the device at defined distances and with defined orientations. This has the advantage of enabling flexible and user-friendly application of the device.

[0009] Another advantage is the design of the device, 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. The evaluation device calculates a time- and location-dependent surface temperature signal Tmess from data of infrared images from the infrared detector array, and the surface temperature signal Tmess is transformed into an image 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.

[0010] The object of the invention is also achieved independently by a method for thermographic component testing of a test object with a device comprising an excitation source, an infrared detector array, a surface scanner, a physical unit for user authentication, a control device and an evaluation device, with the following method steps: authenticating a user, detecting a spatial position and orientation of the device relative to the test object and detecting a surface of the test object with the surface scanner; generating a transient heat flow in the test object through the excitation source; recording infrared images of the surface of the test object with the infrared detector array during a preselected measuring duration;Reconstruction of a spatial position of a defect from acquired data of the infrared images by the evaluation device, wherein for the reconstruction of the defect a surface temperature signal Tmess is calculated from the data of the infrared images by the evaluation device, and wherein the surface temperature signal Tmess is converted into a mirror source representation Ts using a regularization method; q is transformed.

[0011] It is also advantageous if a Green's function based on the heat conduction equation is used in the regularization process.

[0012] The procedure according to which additional information from a group comprising a dimensionality of the heat flow, a number of boundary layers in the test specimen, a position of boundary layers in the test specimen, thermophysical material properties or boundary conditions is used in the reconstruction of the spatial position of the defect.

[0013] According to an advantageous development of the method, the device performs component identification. This makes a priori information about the test object available as additional information.

[0014] According to an advantageous development of the method, the device performs user authentication. This makes the additional information dependent on the device user.

[0015] For component identification as well as for user authentication, technologies such as a contactless transmitter-receiver system (radio frequency identification technologies), optoelectronically detectable fonts, barcodes or multidimensional codes (using a camera or scanner), biometric authentication, including through face, fingerprint, iris or voice recognition, manual authentication via user interface or card reader, or digital authentication based on cryptographic handshake procedures can be used.

[0016] According to an advantageous development of the method, it is provided that an inertial measuring unit is arranged in the device, wherein the infrared detector array of the surface scanners and the inertial measuring unit are arranged relative to each other at defined distances and defined orientations in the device.

[0017] Another advantage is the procedure in which the spatial position and orientation of the device relative to the test object is measured by the inertial measurement unit. This allows the inertial measurement unit to correct and stabilize the image sequence.

[0018] According to an alternative procedure, external data sources are taken into account to record the spatial position and orientation of the device and to record the surface of the test object.

[0019] An advantageous embodiment of the method provides that additional information about the component geometry and the material properties of the test object are used for the optimized discretization of the reconstruction space.

[0020] By the procedure according to which the process steps are repeated one or more times, carried out from a different spatial position and orientation of the device relative to the test object and results of reconstructions of the spatial position of the defect are superposed, the advantage of an even greater accuracy and reliability of the assignment of defects or component features to the three-dimensional geometry of the test object is achieved.

[0021] The further development of the method, according to which temperature and location calibrated image data are calculated from the infrared images, has also proven to be advantageous.

[0022] According to a similarly advantageous procedure, the extracted features of the defects can be used for data compression.

[0023] For a better understanding of the invention, it is explained in more detail using the following figures.

[0024] They show in a highly simplified, schematic representation:

[0025] Fig. 1 shows a device for non-destructive component testing of a test specimen;

[0026] Fig. 2 is a view of the device according to Fig. 1, corresponding to a viewing direction of its sensors or its infrared detector array;

[0027] Fig. 3 is a flow chart of the thermographic component testing process;

[0028] Fig. 4 Details of the regularization process according to the "Regularization and Reconstruction" process step in Fig. 3; Fig. 5 The device during the thermographic examination of the test specimen;

[0029] Fig. 6 shows an illustration of evaluation steps of the measured surface temperature signals carried out with the regularization method using temporal and spatial temperature profiles;

[0030] Fig. 7 Evaluation steps of the regularization procedure of another application case, illustrated by temporal and spatial temperature profiles;

[0031] Fig. 8 shows another embodiment of a device for non-destructive component testing.

[0032] By way of introduction, it should be noted that in the variously described embodiments, identical parts are provided with identical reference symbols or component designations. The disclosures contained throughout the description can be applied analogously to identical parts with identical reference symbols or component designations. Furthermore, the positional information chosen in the description, such as top, bottom, side, etc., refers to the directly described and illustrated figure, and these positional information must be applied analogously to the new position in the event of a change in position.

[0033] Fig. 1 shows a device 1 for non-destructive component testing of a component or test piece 2. The device 1 is suitable for carrying out a component testing method using active thermography and comprises an excitation source 3 and an infrared detector array 4. In addition to an electrical supply unit 5, a control device 6 or a processor unit is also provided for controlling the testing sequence and controlling or regulating the necessary components of the device 1. Part of the control device 6 is formed by an evaluation device 7 for the program-controlled processing of the measurement signals detected by the infrared detector array 4 or by other sensors. Furthermore, the device 1 comprises a surface scanner 8 for recording the geometric data or the geometric features of the test piece 2.Finally, the device 1 is also equipped with an inertial measuring unit 9 for detecting movement of the device 1. Furthermore, the device 1 also comprises an operating terminal 10, which also includes at least one digital, bidirectional communication interface for providing data or retrieving additional information from external data sources. Optionally, the device 1 can also be equipped with an optical camera or an optical imaging system 11 (a so-called RGB camera) for the visible wavelength range.

[0034] Fig. 2 shows a view of the device 1 according to Fig. 1, corresponding to a viewing direction onto the infrared detector array 4 or a viewing direction corresponding to an optical axis 12 of the infrared detector array 4.

[0035] As shown in Fig. 1, the test specimen 2 has an inhomogeneity below a surface 13 in its interior - referred to simply as defect 14.

[0036] Fig. 3 shows a flowchart illustrating the process for thermographic component testing on test piece 2 (Fig. 1). This involves a multi-stage signal processing process. During an initial measurement, the surface 13 of test piece 2 is first optically scanned, and the initial pose (position and orientation) of device 1 relative to test piece 2 or its surface 13 is determined. Before thermally exciting test piece 2 with excitation source 3, one or more static (passive) infrared images of test piece 2 are acquired using infrared detector array 4.

[0037] By determining transformation parameters, one or more coordinate points p can be assigned an image point I(u,v) (Fig. 5). Since no movement of the device 1 occurs between the determination of the position of the device 1, the thermal excitation by the excitation source 3, and the recording of the thermal response by the infrared detector array 4, this assignment also applies to the temporally dynamic part of the infrared radiation emitted by the test object 2 and therefore does not necessarily have to be performed during the measurement of the thermal excitation and response. The coordinate transformation can be determined by a prior calibration of the infrared detector array 4 and the surface scanner 8 or by image registration methods based on characteristic image features, such as edges or differences in emissivity.

[0038] To reduce artifacts in the reconstruction and registration step, the scanner data is consolidated (if necessary, removal of outliers, noise filtering, reduction of image size, reduction of spatial resolution). Furthermore, a normal vector n p for all relevant coordinate points of the surface 13 of the test specimen 2. The normal vectors n p are always oriented towards the interior of the component (Fig. 5).

[0039] After thermal excitation with the excitation source 3, the thermal response of the test specimen 2 is recorded for a sufficiently long time by recording infrared images with the infrared detector array 4. The measurement duration and excitation type determine the desired penetration depth into the test specimen 2. The frame rate when recording the infrared images by the infrared detector array 4 determines the resolution in the axial direction of the device 1, i.e., in the depth direction of the test specimen 2.

[0040] With the aid of the evaluation device 7, the thermal response for each pixel is inversely transformed into an image source representation using a process known as regularization. In this representation, subsurface structures, such as defect 14, are represented as pulse-shaped features in a discrete, equidistant signal I(u,v,w).

[0041] Fig. 3 provides an overview of the measures to be carried out during the thermographic testing procedure and their chronological sequence. After commissioning of the device 1, the spatial position and orientation of the device 1 are determined with the aid of the inertial measurement unit (IMU). Secondly, the external geometry of the test object 2 is measured with the aid of the surface scanner 8. The measurement data obtained in this way provide, as measure 100, information about the geometry as well as the spatial relative arrangement of the device 1 and the test object 2. The measurements according to measure 100 are preferably carried out continuously, i.e., continuously over the entire temporal course of the component testing of the test object 2. This is particularly advantageous in the case where the device 1 is designed as a hand-held device. Section 101 comprises measurements or detections carried out before the thermal excitation of the test object 2.Thus, one or more static (passive) infrared images of the test object 1 are recorded by the thermal excitation with the infrared detector array 4, and transformation parameters are determined for assigning coordinate points p to respective pixels I(u,v) of the infrared images. In a subsequent section 102, the thermal excitation takes place with the aid of the excitation source 3, and simultaneously the thermal response is recorded by recording a sequence of infrared images by the infrared detector array 4. The images recorded by the infrared detector array 4 represent surface temperature data Tmeas(p) from points p on the surface 13 of the test object 2 as well as the temporal progression of the surface temperatures. The surface temperature signals thus obtained are subjected to an evaluation procedure using the evaluation device 7 in a concluding section 103.The surface temperature signals Tmeas are subjected to a so-called regularization process. This enables the reconstruction of defect 14 in test specimen 2.

[0042] Fig. 4 shows a detail of Fig. 3 corresponding to the process step "Regularization and Reconstruction," further specified in Section 103. Following the recording of the thermal response from the surface 13 of the test piece 2 using the infrared detector array 4, a transformation into the aforementioned image source representation takes place. In order to reconstruct defects 14 located on or below the surface 13 or to determine material parameters, the measured surface temperature signal Tmess 6 IR A (Nt x Nu * N v ) into the corresponding mirror source representation Ts q G IR A (N W x N u * N v ) (IR, set of real numbers). Here, N uand N v the number of pixels of the area detector of the infrared detector array 4 in the u- and v-direction. 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 N w The transformation matrix KG IR A (Nt x N w ) can in the simplest case be represented by a Green's function with the thermal diffusivity oi33 in the following form.

[0043] This function corresponds to the fundamental solution of the heat conduction equation. It should also be mentioned here that other Green's functions can also be used depending on the component characteristics and test environment. With the thermal diffusivity in the depth direction 0133 and with the discrete numerical steps ti = 1 A t and tj = j A zand using the running variables 1 = {0, 1, 2, ..., Nt-1} and j = {0, 1, 2, ..., Nw-1}, the elements of the transformation matrix K, using the Green's function given above, can be written as follows: Equation 6 (2) ' The term 77 = describes a dimensionless number, where f is a dimensionless

[0044] Scaling parameter to optimize the quality of the inverse solution and A t which depicts the temporal resolution of the surface temperature signal and A w represents the depth resolution for the image source display.

[0045] By mathematically convolving the transformation kernel or the transformation matrix K with respect to time and space, possible temporal and spatial excitation patterns can be taken into account. Since the diffusion processes and thus the heat conduction equation are considered at the macroscopic level, a linear matrix equation Tmess = K Ts results in the discrete form. q and therefore a linear inverse problem. It can also be observed that equation (2) does not depend on the transverse coordinates u and v. Solving the matrix equation Tmeas = K Tsq therefore represents a local inverse problem. This means that the measured temperature signal can be transformed locally (pixel-by-pixel) into the corresponding image source representation.

[0046] Due to the entropy production during heat diffusion, the transformation requires solving a very ill-posed inverse problem. Various regularization methods can be used for this. It should also be noted that the inverse problem can also be solved using machine learning approaches.

[0047] In this case, the inverse problem is solved by incorporating additional information, such as positivity or sparcity (the solution matrix is ​​sparse), into the evaluation process. The calculated image 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 heat conduction, thermal radiation, or convection, or a mixture of these effects. Furthermore, adiabatic boundary conditions only yield positive image source amplitudes.Thermal radiation and convection can produce both negative and positive image source amplitudes (Figs. 6, 7). Based on the defect characteristics, the depth of defect 14 can only be roughly estimated. Furthermore, due to the boundary conditions and the observation surface, the amplitudes do not provide any relevant information regarding defect 14 and the position of the back wall. Therefore, the amplitudes due to 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 defect depth can now be determined more precisely by evaluating the maximum defect temperature signal.

[0048] From the calculated defect depth, a substitute mirror source signal I 6 IR A (N U x N v x N w) for each pixel of the infrared detector array 4 and each defect signal for a multidimensional defect representation (see Fig. 6f and Fig. 6h).

[0049] Furthermore, if the thermal effusivity of the base material e2 is greater than the thermal effusivity of the defect material ei, the positive amplitude (source) is evident before the negative amplitude (dip) on the depth scale of the image source distribution (Figs. 6c, 6d). If ei is greater than e2, then a dip and then a source are observed on the depth scale (Fig. 7).

[0050] The described transformation of the measured temperature signal into the corresponding image source representation does not take into account transverse diffusion processes or geometric information of the possibly anisotropic and complexly shaped test specimen 2.

[0051] In a further evaluation step, using the geometric information of the test specimen 2 recorded by the surface scanner 8 in conjunction with the known thermal diffusivity tensor a, local filtering can be performed. This allows for a more accurate reconstruction of the true interface dimensions. This local filtering can also be performed by solving an inverse problem. This additional and optional evaluation step is particularly recommended for defects 14 that lie deep beneath the surface 13 of the test specimen 2 and when the ratio of the thermal diffusivities in the transverse direction and the depth direction is large.

[0052] As shown in Fig. 4, the surface temperature signals undergo a sequence of steps for their evaluation, regularization, and defect reconstruction. A first step 111 corresponds to the detection of the surface temperature signal T mess. In the subsequent step 112, the measured surface temperature signal Tmess is transformed into the corresponding image source distribution according to the linear matrix equation Tmess = K Ts q In a subsequent step 113, defect features are extracted. In step 114, the defect temperature signal is calculated based on the extracted defect features. The following step 115 corresponds to the determination of the real defect depth from the calculated defect temperature signal. The real defect depth is used in a subsequent step 116 to calculate a substitute mirror source signal. In a final step 117, local filtering is performed based on the measured geometry information of test piece 2 and information about the known thermal conductivity tensor. Based on the results obtained, the size determination of defect 14 in test piece 2 can be improved or optimized.

[0053] Fig. 5 shows the device 1 performing the thermographic examination of the test specimen 2.

[0054] With the help of the excitation source 3, the test piece 2 has already been thermally excited, and the thermal response of the component is detected by the infrared detector array 4 of the device 1 over a predetermined measurement period, and the geometric information is acquired with the help of the surface scanner 8 of the device 1 (Fig. 1, 2). This means that a sequence of thermal images is recorded by the infrared detector array 4. Based on this, a 3D depth signal Ik(u,v,w) is reconstructed from the at least one spatial position k of the device 1. Assuming that for a surface point pi and an associated, normalized normal vector n Pi at least one image position (u p *, v p *) must be present. Relevant substitute feature positions from the depth signal Ik(u p *,v p*,w) are projected into a global coordinate system.

[0055] For known material parameters (for example in the form of a thermal conductivity sensor) and for normal vectors that are implicitly known from the surface 13 or manually defined, a relevant substitute feature P in the global coordinate system is composed of its position and its intensity. The projection of substitute feature positions of one or more data sets I(u,v,w) into the global coordinate system leads, according to Fig. 5, to an aggregation of substitute features on or near the position of the physical component feature, provided that the substitute features belong to the same component feature (defect 14). This is the case, for example, with neighboring surface points from a dimensioning or congruent or neighboring surface points from multiple measurements (i.e., multiple execution of steps 101, 102, and 103, see Fig. 3) from congruent or different spatial positions of the device 1. Such an aggregation of substitute features is also the case when surface points from multiple measurements (i.e., multiple execution of steps 101, 102, 103, see Fig. 3) from opposite spatial positions of the device 1, if the component to be tested or the test specimen 2 is sufficiently thin orthe measurement of the temperature response is sufficiently long. In such a case, the opposite, invisible component surface is visible as a substitute feature in each measurement.

[0056] The thermographic measurements to be superimposed may differ from each other with regard to all common measurement parameters, as these can be considered as preliminary information in the reconstruction and positioning of the substitute features. These measurement parameters can include: the temporal and spatial excitation pattern 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 distance of the surface 13 of the test object 2 or the spatial position of the device 1.

[0057] If the transformed data is saved in the form of a point cloud (i.e., the coordinates are explicitly available), the depth resolution can be defined independently of the resolution of the geometry capture by the surface scanner 8. In addition to the substitute feature position, the substitute feature intensity can also be assigned to a data point. This enables improved visualization of the features of the test object 2, for example, by texturing point clouds or rendered surfaces of the features or defect 14. Furthermore, this enables the specification of intensity values ​​if the result data is to be presented in a three-dimensional, Cartesian grid with implicit coordinates (volume pixels, or "voxels" for short). The substitute feature positions of relevant data points are transferred into the grid by interpolation.By systematically positioning the substitute features in the global coordinate system, further reconstruction and segmentation methods can be applied in a subsequent step, enabling more precise localization and visualization of the component features. The inclusion of additional geometric (surface points, normal directions, prior information on the features of test piece 2 such as orientation, size, and shape) and material-specific information also enables the application of well-known methods based on superposition, triangulation, lateration, regression, and machine learning.

[0058] The method for thermographic reconstruction of the defect 14 is explained in more detail below with reference to Figs. 6 and 7.

[0059] Fig. 6 illustrates the evaluation procedure for reconstructing a defect 14 and for determining material parameters of the test specimen 2. Diagram a) shows, as an example, a defective test specimen 2, where the thermal effusivity of the base material e2 is greater than the thermal effusivity of the defective material ei. Diagram b) shows a characteristic temperature signal for a measurement in reflection mode in the defect region. Diagram d) shows the resulting image source distribution. Diagram c) shows the extracted defect and component features, diagram e) the resulting defect temperature signal, and diagram f) the corrected depth distribution of the image sources. Diagrams g) and h) show a 2D visualization of the extracted defect and component features and the corrected depth distribution of the image sources.

[0060] The described process steps correspond to the measures already presented in Fig. 4. The characteristics of component features of test piece 2 depend on the thermal impedances of the base material and the defect material, whereby the thermal impedances are formed by the respective effusivities, which in turn depend on the thermal conductivity k, the specific heat capacity c pand the material density p (Fig. 6a). The greater the difference in thermal impedances, the more strongly the characteristic component features of the test piece 2 are displayed. Since the position of the image points in space relative to one another is known, the individual feature intensities and positions can be used for more precise feature localization and quantification through suitable superposition. Component features beneath the surface 13 of the test piece 2, which are imaged, for example, by a defect interface, can appear in several adjacent image points due to their spatial extent, with a simultaneous distortion of the spatial extent due to heat diffusion. Fig.Figure 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 with respect to time.

[0061] However, the described procedure for thermographic defect reconstruction is applicable to all temporal and spatial thermal excitation functions, as well as in the case of a transmission configuration (excitation source 3 and infrared detector array 4 on opposite sides).

[0062] To ensure that all relevant component characteristics are captured, a test measurement can be performed. With a test measurement, the thermal diffusion time td and thus the measurement time t me ss can be determined.

[0063] The temperature signal measured after thermal excitation can be transformed locally, i.e., pixel-by-pixel, into a corresponding image source representation. The diagram shown in Fig. 6d illustrates the calculated image source distribution as a function of depth and reveals the characteristics of the measured surface temperature signal with respect to component interfaces and defect interfaces in the form of sources (recognizable by positive amplitudes) and sinks (recognizable by negative amplitudes). By extracting the amplitudes due to defect 14 and the backplane, the diagram shown in Fig. 6c is finally obtained.

[0064] A noise-free defect temperature signal is then calculated from the resulting signal. By evaluating the maximum defect temperature signal of the filtered defect temperature signal, the defect depth can then be determined more precisely. This is illustrated in Figs. 6e and 6g, with the diagram in Fig. 6g showing a cross-section through the surface 13 of test piece 2 with the corresponding image source distribution.

[0065] From the calculated defect depth, a substitute mirror source signal IE IR A (N U x N - x N w ) can be determined for each camera pixel and defect signal for a multidimensional defect representation, as illustrated by the representations in Figs. 6f and 6h. In the example according to Fig. 6, the thermal effusivity of the base material e2 is assumed to be greater than the thermal effusivity of the defect material ei.

[0066] Fig. 7 illustrates the evaluation procedure for reconstructing defects 14 and determining material parameters. In contrast to the case treated in Fig. 6, the thermal effusivity of the base material e2 is smaller than the thermal effusivity of the defect material ei. The temperature signal was recorded in reflection mode. Diagram a) shows the corresponding image source distribution in the defect region. Diagram b) shows the extracted defect and component features, and diagram d) the defect temperature signal calculated from them. Diagram c) shows the corrected depth distribution of the image sources. Fig. 7 thus shows an example of thermographic defect reconstruction for a case where the effusivity of the defect material el is greater than the effusivity of the base material e2. In this case, a dip and then a source emerge on the depth scale (Fig. 7a and Fig. 7b).

[0067] For consistent registration of new thermographic data and the reconstruction of component features of test piece 2 from this data, the geometric features captured by surface scanner 8 (e.g., by a TOF camera, profile scanner, or by the device itself by overlaying image data from different poses) and the position estimates from the inertial measurement unit are used. The position estimation of the sensor in the original coordinate system is performed continuously.

[0068] A preferred embodiment of the device 1 is that of a hand-held device on which all required components are positioned at defined, relative distances from one another.

[0069] In an alternative embodiment of the device 1, the spatial position relative to the component surface or the surface 13 of the test piece 2 is provided by external data sources, for example by a single- or multi-axis robot that moves the device 1.

[0070] In a further alternative embodiment of the device 1, the surface 13 of the test object 2 and the inertial position of the device 1 relative to the surface 13 are provided by external data sources, for example, by a CAD model of the test object 2. In a likewise alternative embodiment, the device 1 remains stationary and the test object 2 is moved by suitable devices. The relative position of the device 1 is determined by the manipulation device of the test object 2 and provided to the device 1 for evaluation.

[0071] With renewed reference to the illustration of the device 1 according to Fig. 1, a further possible application or embodiment of the device for thermal component testing is described below. Prior to the method steps already described, the test object 2 is initially identified. This means that the control device 6 or the evaluation device 7 is designed to detect or automatically read an identifier 15, coded in some form, which is applied to the surface 13 of the test object 2. This enables the results of the thermographic component test to be subsequently unambiguously assigned to the respective test object 2. Images of the identifier 15, preferably taken by the infrared camera or the infrared detector array 4, serve as the basis for this.Alternatively, it is also possible to capture the images required for component identification using the optical camera 11. The control device 6 or the evaluation device 7 is designed for program-controlled decoding of the image information data and identification of the identifier 15. In this embodiment of the device 1, the identifier 15 can be formed by optoelectronically detectable writing or one- or multi-dimensional codes (such as a barcode or a QR code).

[0072] In a preferred embodiment of the device 1, a program-controlled user check 16 is provided in the control device 6. With this embodiment of the device 1, it is therefore possible to restrict its operation to a group of authorized persons. For example, user authentication can be carried out by checking a password entered by the user via the operating terminal 10. Alternatively, user authentication can also be carried out on the basis of technologies such as facial recognition or iris recognition. The infrared camera or the infrared detector array 4 or the optical camera 11 of the device 1 are alternatively available for recording corresponding images. The program-controlled user check 16 of the device 1 could alternatively also be based on other technologies, such as reading an RFID element with corresponding readers orScanners and fingerprint recognition can be used. Depending on the technology used, the user verification 16 of the device 1 is designed for image recognition or for decoding corresponding detection signals from a corresponding reader. Another embodiment of user identification could also provide for two- or multi-factor authentication.

[0073] A further embodiment of a device 1 for thermographic component testing is described below with reference to Fig. 8. Fig. 8 shows a device 1 for non-destructive component testing of a test piece 2, which also comprises two excitation sources 3 for generating a heat flow in the test piece 2 and a control device 6 with an evaluation device 7. In this example of the device 1, two infrared detector arrays 4 are provided for detecting or recording the thermal radiation emitted by the surface 13 of the test piece 2. This allows a combined evaluation of the surface temperature signals obtained from the respective infrared images of the two infrared detector arrays 4, which subsequently makes it possible to achieve greater spatial and temporal resolution.

[0074] On the other hand, the type of thermal excitation of the surface 13 of the test piece 2 caused by the excitation sources 3 can also improve the quality of the component testing results determined using the method. One possibility for this is to provide a filter 17 in the respective beam path of the two excitation sources 3. The two filters 17 select a narrower spectral range from the infrared radiation generated by the two excitation sources 3.

[0075] Another embodiment provides for a focusing device or a focusing lens 18 to be provided in the beam path of the respective excitation source 3. The focusing lenses 18 allow the component testing method to be carried out in such a way that the thermal excitation with the infrared radiation can be concentrated locally on the surface 13 of the test piece 2. By appropriately designing or adjusting the focusing lenses 18, for example, the heat input can be concentrated on a surface area that is as point-shaped or linear or segment-shaped as possible. This is advantageous for the evaluation method to be used in that the initial conditions of the heat flow generated in the test piece 2 can be determined or specified more precisely.

[0076] The embodiments show possible embodiments, whereby it should be noted at this point that the invention is not limited to the specifically illustrated embodiments thereof, but rather various combinations of the individual embodiments with each other are also possible and this possibility of variation lies within the skill of the person skilled in the art in this technical field due to the teaching of technical action by means of the objective invention.

[0077] The scope of protection is determined by the claims. However, the description and drawings must be used to interpret the claims. Individual features or combinations of features from the various embodiments shown and described may represent independent inventive solutions. The problem underlying these independent inventive solutions can be derived from the description.

[0078] All information on value ranges in this description is to be understood as including any and all sub-ranges thereof, e.g. the information 1 to 10 is to be understood as including all sub-ranges starting from the lower limit of 1 and the upper limit of 10, ie all sub-ranges begin with a lower limit of 1 or greater and end with an upper limit of 10 or less, e.g. 1 to 1.7, or 3.2 to 8.1, or 5.5 to 10.

[0079] For the sake of clarity, it should finally be pointed out that, in order to better understand the structure, some elements have been shown out of scale and / or enlarged and / or reduced in size.

[0080] Reference symbol list

[0081] Device 100 Measure Test object 101 Section Excitation source 102 Section Infrared detector array 103 Section Supply unit 111 Step Control device 112 Step Evaluation device 113 Step Surface scanner 114 Step Inertial measuring unit 115 Step Operating terminal 116 Step Camera 117 Step Optical axis Surface Defect Identification

[0082] User verification filter

[0083] Focusing lens

Claims

Patent claims 1. Device (1) for thermographic component testing, - comprising an excitation source (3) for generating a transient heat flow in a test specimen (2), - an infrared detector array (4) for detecting a surface (13) of the test object (2) emitted thermal radiation, - a surface scanner (8), - a control device (6) and an evaluation device (7), characterized in that the device (1) comprises an inertial measuring unit (9) for detecting movements of the device (1).

2. Device (1) according to claim 1, characterized in that a geometry detection system is designed to calculate spatial coordinates of the surface (13) of the test object (2).

3. Device (1) according to claim 2, characterized in that the geometry detection system is designed to calculate a relative spatial position between the device (1) and the test object (2).

4. Device (1) according to one of the preceding claims, characterized in that the infrared detector array (4), the surface scanner (8) and the inertial measuring unit (9) are arranged relative to one another at defined distances and defined orientations in the device (1).

5. Device (1) according to one of the preceding claims, characterized in that two infrared detector arrays (4) are designed to detect the thermal radiation emitted by a surface (13) of the test object (2).

6. Device (1) according to one of the preceding claims, characterized in that two excitation sources (3) are formed.

7. Device (1) according to one of the preceding claims, characterized in that the excitation source (3) comprises a filter (17) for selecting a part of a spectral range of the infrared radiation.

8. Device (1) according to one of the preceding claims, characterized in that the excitation source (3) comprises a focusing lens (18).

9. Device (1) according to one of the preceding claims, characterized in that the control device (6) and / or the evaluation device (7) is designed for program-controlled identification of the test object (2) by decoding an identifier (15) applied to the surface (13) of the test object (2).

10. Device (1) according to one of the preceding claims, characterized in that a user check (16) is included and the user check (16) is designed for program-controlled authentication of a user, wherein a method for authenticating the user is used which is selected from a group comprising entering a password, performing facial recognition or eye iris recognition, detecting a fingerprint, reading an RFID element, or reading a user card provided with a magnetic strip.

11. Device (1) according to one of the preceding claims, characterized in that the evaluation device (7) is designed for the program-controlled reconstruction of a defect (14) in a test object (2), wherein a surface temperature signal Tmess is calculated by the evaluation device (7) from data of infrared images from the infrared detector array (4), and wherein the surface temperature signal Tmess is converted into a mirror source representation Ts using a regularization method q is transformed.

12. Method for thermographic component testing of a test specimen (2) with a device (1) comprising an excitation source (3), an infrared detector array (4), a surface scanner (8), a control device (6) and an evaluation device (7), with the following method steps: - Detecting a spatial position and orientation of the device (1) relative to the test object (2) and detecting a surface (13) of the test specimen (2) with the surface scanner (8); - generating a transient heat flow in the test object (2) by the excitation source (3); - recording infrared images of the surface (13) of the test specimen (2) with the infrared detector array (4) during a preselected measuring period; - reconstruction of a spatial position of a defect (14) from acquired data of the infrared images by the evaluation device (7), - characterized in that for the reconstruction of the defect (14) a surface temperature signal Tmess is calculated by the evaluation device (7) from the data of the infrared images, - and that the surface temperature signal Tmess is converted into a mirror source representation Ts q where Tmess = K Tsq, and where K is a transformation matrix.

13. Method according to claim 12, characterized in that substitute mirror sources and / or substitute features are calculated from the mirror source representation Tsq.

14. The method according to claim 13, characterized in that a regularization method or a machine learning approach is used to calculate the substitute mirror sources and / or substitute features.

15. Method according to claim 14, characterized in that Green's functions based on the heat conduction equation are applied in the regularization method.

16. Method according to one of claims 12 to 15, characterized in that in the reconstruction of the spatial position of the defect (14) additional information from a group comprising a dimensionality of the heat flow, a number of boundary layers in the test piece (2), a position of boundary layers in the test piece (2), thermophysical material properties or boundary conditions is used.

17. Method according to one of claims 12 to 16, characterized in that an inertial measuring unit (9) is arranged in the device (1), wherein the infrared detector array (4) of the surface scanner (8) and the inertial measuring unit (9) are arranged relative to one another at defined distances and defined orientations in the device (1).

18. Method according to one of claims 12 to 17, characterized in that the spatial position and the orientation of the device (1) and the test object (2) relative to each other are measured by the inertial measuring unit (9).

19. Method according to one of claims 12 to 18, characterized in that external data sources are taken into account for detecting the spatial position and orientation of the device (1) and for detecting the surface (13) of the test object (2).

20. Method according to one of claims 12 to 19, characterized in that in the method step of recording the infrared images of the surface (13) of the test object (2), the infrared images are recorded by two infrared detector array cameras (4).

21. Method according to one of claims 12 to 20, characterized in that in the method step of generating a heat flow in the test object (2) by the excitation source (3), infrared radiation emitted by the excitation source (3) is selected by a filter (17) to have a reduced spectral range of the infrared radiation.

22. Method according to one of claims 12 to 21, characterized in that in the method step of generating a heat flow in the test object (2) by the excitation source (3), infrared radiation emitted by the excitation source (3) acts on the surface (13) of the test object (2) by a focusing device or a focusing lens (18) in a manner that is as concentrated as possible in a point-like and / or linear or section-like manner.

23. Method according to one of claims 12 to 22, characterized in that additional information about the component geometry and the material properties of the test object (2) is used for the optimized discretization of the reconstruction space.

24. Method according to one of claims 12 to 23, characterized in that the method steps are repeated one or more times, are carried out from a different spatial position and orientation of the device (1) relative to the test object (2) and results of reconstructions of the spatial position of the defect (14) are superposed.

25. Method according to one of claims 12 to 24, characterized in that temperature and location calibrated image data are calculated from the infrared images.

26. Method according to one of claims 12 to 25, characterized in that the extracted features of the defects (14) or boundary layers are used for data compression.

27. Method according to one of claims 12 to 26, characterized in that the method step of detecting the spatial position and orientation of the device (1) relative to the test object (2) is preceded by a component identification by detecting an identifier (15) attached to the test object (2).

28. Method according to one of claims 12 to 27, characterized in that the method step of detecting the spatial position and orientation of the device (1) relative to the test object (2) is preceded by a user authentication, wherein the user authentication comprises a method selected from a group comprising entering a password, performing facial recognition or eye iris recognition, detecting a fingerprint, reading an RFID element, or reading a user card provided with a magnetic strip.

Images