Device, system and method for inspecting 3-dimensional objects

Abstract

The invention relates to a device (1, 101) for inspecting 3-dimensional objects, for example pouch cells (11, 31, 111), and a corresponding method, wherein each object has a housing with a top side and a bottom side, wherein the upper side of the housing (12, 112) is composed of at least one upper surface section (13, 113) and a plurality of lateral surface sections (17, 18, 21, 22, 23, 24, 117, 118, 121, 122, 123, 124, 125, 126, 127, 128, 129), wherein the device • a motion detection device, • a line illumination device (51) for illuminating a line-shaped area of the upper side of the object to be inspected, • a surface illumination device (52, 53, 55, 56) for illuminating the entire upper side of the housing of the object to be inspected from above in its rest state, • a matrix camera (40) arranged above the rest position of the object to be inspected in each case for capturing image information in a field of view, wherein the field of view ◯ for line-by-line detection of the light of the line illumination device reflected from the line-shaped area of the upper side in the moving state of the object to be inspected and ◯ is designed for the matrix-wise detection of the light reflected upwards from the entire upper side of the area illumination device in the rest state of the object to be inspected, and wherein a data processing device (70, 170) is provided which determines the presence of an error of at least one error type from the acquired image information and / or determines a quality index which allows an assessment of the quality of the object.

Description

[0001] The invention relates to a device which is suitable for the inspection of 3-dimensional objects, in particular of so-called pouch battery cells (hereinafter referred to as pouch cells), as well as a corresponding method.

[0002] Pouch cells are a type of battery used primarily for lithium-ion batteries. A pouch cell typically consists of a pillow-like casing or package made from a plastic-coated metal foil (e.g., aluminum foil). Therefore, such a cell is also referred to as a polymer battery. The casing is designed as a flexible, flat, and lightweight, sealed pouch or cushion. Inside the casing, there is typically a stack of superimposed electrode layers, active layers, and separator layers. The terminals are formed as two tabs that protrude from the pillow-like casing on one side, adjacent to one another, or on opposite sides. Pouch cells are known for their high energy density, compact design, and flexibility, making them suitable for a variety of applications, including electric vehicles.The pouch cells can be easily scaled up or down to meet the specific requirements of different electric vehicle models. Their flat and flexible design also allows for easier integration into various areas of the vehicle, resulting in more efficient packaging and improved space utilization. A disadvantage of the pouch cell design is that, due to their construction, they are generally sensitive to mechanical damage. This can easily release gases or electrolyte, cause severe cell expansion, or lead to internal short circuits.

[0003] It is therefore desirable to inspect such and other 3-dimensional objects thoroughly during quality control in order to detect damaged objects at an early stage.

[0004] Various options for quality control of flat objects such as battery cells have already been disclosed. For example, document US 2022 / 0 390 387 A1 discloses a method in which optical coherence tomography (OCT) is used to inspect a gap between a lead foil and a terminal tab of a pouch cell. This allows a statement to be made about the quality of the pouch cell's closure, but this has very limited significance for the quality of the pouch cell. Document EP 4 117 081 A1 describes a very complex inspection system comprising a thickness measuring unit, a unit for measuring electrical properties, a printing unit, a tab cutting unit, a weighing unit, a tab testing unit, and a defect selection unit.The thickness measuring unit measures the thickness of the pouch cell, and the printing unit is used to print information about the pouch cell onto its surface. The tab inspection unit determines the length and shape of the tab using vision inspection. The defect selection unit sorts out defective pouch cells into magazines provided for this purpose. Document DE 10 2019 109 703 A1 shows and describes an arrangement for quality testing a battery cell whose transparent outer skin encloses an interior space. Within the interior, i.e., beneath the outer skin, an (additional) glass pin or a lithium metal plate is arranged, which, in the presence of a predetermined hydrogen fluoride concentration, changes the optical appearance in this space.Accordingly, this glass pin or these lithium metal flakes are analyzed in a complex manner using optoelectronic measurement in order to determine the hydrogen fluoride concentration and thus the quality of the battery cell. Finally, document EP 3 869 603 A1 specifies a method for testing laminated electrode-separator assemblies and batteries with electrode-separator assemblies with regard to their quality, which is suitable for large-scale production and ensures verification of the secure and reliable connection of the layers to one another. The test involves detecting at least part of a surface of the electrode-separator assemblies using a detection device to generate a measurement result and evaluating the measurement result. The detection device is particularly suitable for detecting the surface topography, surface temperature, and / or surface color.This can be done using an optical sensor, a camera, and / or a camera. The detection device can comprise at least one illumination unit that can emit light onto the surface of the electrode-separator assembly to be detected. The evaluation can include image processing and / or image analysis.

[0005] Further systems for inspecting objects are known from the documents DE 10 2022 205 760 A1, CN 1 11 965 185 A, DE 10 2020 109 945 A1 and DE 10 2011 113 670 A1.

[0006] The above-mentioned known methods are either comparatively complex or only allow a very limited assessment of the quality of a 3-dimensional object, in particular a pouch cell. Therefore, the object of the present invention is to provide a simple and cost-effective device for inspecting an object that allows a comprehensive assessment of the quality of this object. Similarly, the object of the invention is to provide a corresponding inspection method.

[0007] The above object is achieved by the device for inspecting a 3-dimensional object, in particular a battery cell, preferably in the form of a pouch cell, having the features of claim 1 and a corresponding inspection method having the features of claim 11.

[0008] In particular, the object is achieved by a device for inspecting 3-dimensional objects, in particular battery cells in the form of pouch cells, wherein each object has a substantially pillow-shaped or cuboid-shaped housing with a top side and a bottom side (the housing may include a first projecting connection tab (hereinafter referred to as tab) and possibly at least one second projecting connection tab (hereinafter referred to as tab), wherein the upper side of the housing is composed of at least one (for example, substantially horizontally arranged) upper surface section and a plurality of lateral surface sections which extend obliquely or perpendicularly to the at least one upper surface section or represent corner sections, wherein the underside of the housing is composed of at least one (for example, substantially horizontally arranged, parallel to the upper surface portion) lower surface portion on the underside and a plurality of lateral surface portions which run obliquely or perpendicularly to the at least one lower surface portion or represent corner portions, wherein the device • a motion detection device which, with respect to each object to be inspected, detects motion information with respect to a relative movement to a line illumination device (motion state) and with respect to an arrangement in a predetermined position and over a predetermined period of time with respect to an area illumination device (rest state), • the line illumination device for illuminating a line-shaped area of the top side of the object to be inspected, • the surface illumination device for illuminating the entire top side of the housing of the object to be inspected from above in its resting state, • if necessary, at least one first deflection mirror arranged next to each side of the housing when the object is at rest, • a matrix camera arranged above the rest position of the object to be inspected for capturing image information in a field of view, wherein the field of view is ◯ for line-by-line detection of the light of the line illumination device reflected from the line-shaped area of the upper side in the moving state of the object to be inspected and ◯ for the matrix-wise detection of the light of the area illumination device reflected upwards from the entire upper side in the rest state of the object to be inspected, including the light reflected from the lateral surface sections, optionally via the at least one first deflecting mirror into the matrix camera, and wherein a data processing device is provided which is configured to receive and process the image information recorded by the matrix camera, wherein the data processing device assigns the image information recorded line by line in the moving state and the image information recorded matrix by matrix in the rest state to the respective object to be inspected and determines the presence of a defect of at least one defect type from this image information and / or determines a quality index which allows an assessment of the quality of the object.

[0009] The device is used to inspect 3-dimensional objects, for example flat objects in pillow or cuboid shape, in particular in the shape of battery cells, preferably pouch cells. In one embodiment, the present invention can be used for a flat object, wherein a flat object is referred to as a 3-dimensional object that has a significantly smaller dimension in one spatial direction (e.g. height) than in the other two spatial directions and therefore essentially has the shape of a flat cuboid or a pillow shape or a shape similar to these shapes. Alternatively, the dimension in one spatial direction can also be larger, so that the object is referred to as essentially cuboid-shaped. Here, “essentially” means that the shape of the object approximates that of a pillow or a cuboid. For example, the cuboid can have strongly bevelled edges.In many cases, such an object also has a first connection tab (tab for short, e.g. the anode) and optionally at least one second connection tab (tab for short, e.g. the cathode), each of which protrudes laterally. Each object has a housing with a top side and a bottom side opposite the top side, wherein any projecting tabs present belong to the housing. The device according to the invention can be used both for inspecting 3-dimensional objects that have one or more such tabs and for inspecting 3-dimensional objects without such tabs. In particular, the device is suitable for flat objects that have step-shaped or terrace-shaped sections, in particular on their edge, or optionally the aforementioned connection tabs.The object is therefore viewed in such a way that one of the two largest sides forms the upper side and the opposite, equally large side forms the lower side. If the upper side is on top and the lower side is on the bottom, then the upper side of the housing has at least one substantially horizontally running upper surface section, which is the surface section of the upper side with the greatest extent. Further horizontally running surface sections can be provided which run parallel to the upper surface section of the upper side, for example a terraced surface section of the upper side. The upper side further has a plurality of lateral surface sections which run obliquely, parallel or perpendicular to the at least one upper surface section (e.g. edges or side surfaces) or represent corner sections.The lateral surface sections also include the sections running parallel to the upper surface section or a surface of a projecting tab (tab surface). Accordingly, the underside of the housing has at least one substantially horizontally running lower surface section, which is the surface section with the greatest extent. Further horizontally running surface sections running parallel to the upper surface section of the underside, for example a terraced surface section of the underside, can be provided. The underside further has a plurality of lateral surface sections running obliquely, parallel to, or perpendicular to the at least one upper surface section (e.g., edges or side surfaces) or representing corner sections.The first tab and the optionally present at least one second tab can, for example, protrude from a short side and / or a long side and each have an upper tab surface and a lower tab surface. For example, the first tab and the second tab protrude from a single short or long side. In this case, they are arranged next to each other. Alternatively, the first tab and the second tab can protrude from opposite short or long sides. The housing can have a substantially rectangular shape (without taking into account any tabs that may be present) when viewed from the top or bottom of the housing. The short side is the short side of this rectangle and the long side is the long side of this rectangle.

[0010] The movement of the 3-dimensional objects to be inspected takes place by means of a movement device which causes the relative movement of the objects to be inspected to the line illumination device at a predetermined speed (movement state), for example essentially parallel to the upper surface section, for example in the direction of the greatest extent of the upper surface section (length) or transversely thereto. The predetermined speed is, for example, at least 500 mm / s, e.g. at least 800 mm / s. Furthermore, the movement device is configured such that, upon further movement of the respective object, it causes the object to be arranged at rest in a predetermined position and for a predetermined period of time with respect to an area illumination device (rest state). The predetermined period of time for which the object is arranged in the rest state can be before or after the movement state.The predetermined period of time in the idle state can, for example, be at least 300 ms, e.g., at least 400 ms. In one embodiment, the movement device is implemented by a carriage which can be moved in a predetermined manner on a linear unit. The carriage has, for example, suction cups by means of which the housing of the object can be attached to the carriage on its underside. The movement information relating to the movement of the 3-dimensional object to be inspected (i.e., its arrangement in the moving state and in the idle state) is recorded by a movement recording device and transmitted to the data processing device. There, the recorded movement information is used together with the image information recorded by the matrix camera to determine the presence of defects and / or the quality indicator.

[0011] The linear illumination device illuminates a linear region of the upper side of the cushion-like housing (optionally including the upper tab surface of the first tab and the second tab). For example, the linear illumination device is formed by a lamp with a plurality of LEDs arranged to illuminate a desired linear region. In this case, one LED row or, for a wider linear region, several adjacent LED rows (e.g., 2 to 10 LED rows) can be provided. In one embodiment, the linear illumination device is switched such that it illuminates each point of the linear region with light at two different intensities (i.e., with high intensity A and with low intensity B).Accordingly, the line-by-line detection of the light reflected from the linear area of the upper side (possibly including the upper tab surface of the first tab and / or the second tab) takes place with an adapted switching rhythm in the form ABABAB... (i.e. the two different intensities A, B are switched alternately). The line-by-line detection of the image information (frequency of detection and time of detection) and the feed speed of the movement device are synchronized for this purpose. This lighting is also referred to as HDR reflection bright field lighting.

[0012] The area illumination device illuminates the entire top surface of the housing (if applicable, including the upper tab surface of the first tab and the second tab) of the objects to be inspected from an angle above. For example, the light from the area illumination device strikes the top surface of the housing (if applicable, including the upper tab surface of the first tab and the second tab) at an angle of incidence in the range of 10° to 60° with respect to the horizontal direction. Using the oblique illumination provided by the area illumination device, defects such as notches, protrusions, scratches, folding defects, edge cracks, sealing defects, and similar topological defects can be easily detected. Defects in the form of absorbing defects (e.g., contamination, foreign bodies on the surface) can also be detected. The area illumination device is implemented using LED spotlights or other quasi-point emitters.In one embodiment, at least one second deflecting mirror is arranged above the position of the object to be inspected in the resting state. This second deflecting mirror runs perpendicular to the horizontal direction and deflects the light from the area illumination device so that it falls obliquely from above onto the top of the pocket-shaped housing (optionally with tabs). This allows the overall external dimensions of the inspection device to be made smaller.

[0013] The inspection device is characterized in that, using a single matrix camera, the reflected light of the linearly illuminated area of the upper side of the housing of the object to be inspected in the moving state is captured line by line in the form of image information, and the reflected light of the entire upper side of the housing (optionally including the upper tab surface) of an object to be inspected arranged in the stationary state, illuminated obliquely from above, is captured matrix by matrix in the form of image information (intensity and, in one embodiment, additionally a color value). This captured image information (image data) is transmitted to the data processing device. The matrix camera is arranged above the object when the object is in its stationary state at the specified position.Line-by-line acquisition represents a sub-area of the field of view of the matrix camera and results in one or more adjacent pixel lines (e.g., 16 to 128 pixel lines) with image information, while matrix-by-matrix acquisition results in a pixel matrix with image information, with the pixel matrix also representing a sub-area of the field of view. In one embodiment, the image information can be determined in a predetermined wavelength range. The field of view of the matrix camera is designed such that image information acquired matrix-by-matrix and pixel-by-pixel can be acquired using a single matrix camera, which is then assigned to the respective object by the data processing device.

[0014] The matrix camera can be designed, for example, as a CCD or CMOS camera. The matrix camera records the light intensity of a large number of pixels in the field of view, which pixels are arranged in rows and columns, i.e. in a matrix. For this purpose, the matrix camera has a light-sensitive element (e.g. a CCD or CMOS sensor) for each pixel. The size of the area recorded by each light-sensitive element determines the resolution of the matrix camera. The matrix camera can, for example, have a field of view of 9344 x 7000 pixels or 8192 x 8192 pixels and can therefore record image information with 805 x 603 pixels matrix-wise within the field of view. The line-by-line recording can accordingly cover a range of 16 to 128 x 1000 to 8192 pixels.The matrix camera is further positioned to view the object to be inspected vertically from above in the idle state, ensuring a sharp view of this section of the field of view. The matrix camera is focused to achieve the most uniform sharpness possible across the entire field of view. This is particularly true for a line of sight, where the image information from the object is fed into the matrix camera via mirrors. This is achieved by a corresponding aperture setting, which achieves the necessary depth of field.

[0015] In one embodiment, the matrix camera is configured (e.g., controlled by the data processing device) such that the line-by-line and matrix-by-matrix acquisition of the image information occurs in an acquisition sequence (temporal sequence of a sequence of images taken by the matrix camera across its entire field of view). This can be synchronized with appropriate control of the illumination (i.e., the line illumination device and / or the area illumination device). In one embodiment, the image information to be acquired line-by-line of a first object to be inspected (in a moving state) can be acquired at least partially simultaneously with the image information to be acquired matrix-by-matrix of a second object to be inspected (in a stationary state) that is different from the first object. Such a configuration of the acquisition sequence can shorten the total time required for the quality assessment of the object.This means that the matrix camera is set up in such a way that at least one of its images is part of an acquisition sequence. • the line-by-line image information from the line-shaped area of the upper side (optionally including the upper tab surface of the first tab and / or the second tab) reflected light of the line illumination device in the moving state of a first object and • the image information recorded in a matrix-wise manner from the entire upper side (optionally including the upper tab surface of the first tab and the second tab) contains the light of the area illumination device reflected upwards in the rest state of a second object and the light reflected from the lateral surface sections of the second object via the at least one first deflection mirror into the matrix camera, wherein the second object is different from the first object (ie in the same recording).Acquisition and illumination sequences may, for example, include a plurality of line-by-line acquisitions of the light reflected from the linear region of the upper surface (optionally including the upper tab surface of the first tab and / or the second tab) (for example, between 50 and 120 line-by-line acquisitions) and, optionally partly in the same recording, several (between 5 and 20) matrix-by-matrix acquisitions of the entire upper surface (optionally including the upper tab surface of the first tab and the second tab). Alternatively, the image information to be acquired line-by-line and the image information to be acquired matrix-by-matrix of two different objects may be acquired consecutively by the matrix camera in the acquisition sequence. In this case, in order to save time, only sections of the entire pixel matrix of the matrix camera may be read out, e.g.the corresponding section of the line-by-line recording and the corresponding section of the matrix-by-matrix recording.

[0016] When capturing image information, the matrix camera is stationary (i.e., it does not move, nor do any parts of it), and the dimensions of the field of view of the matrix camera are designed so that both the image information to be captured line by line and the image information to be captured matrix by matrix are contained in the same field of view. During line-by-line capture, the object to be inspected is in a state of motion, i.e., the object to be inspected continues to move while the image information is being created. In contrast, during matrix-by-matrix capture, the object to be inspected is at rest in a predetermined position for a predetermined period of time (i.e., in a rest state), allowing precise determination of the image information captured matrix by matrix.Furthermore, in addition to the object to be inspected, at least two first deflecting mirrors are provided, which are also captured by the field of view of the matrix camera and which provide further image information of the lateral surface sections of the upper side of the object to be inspected. These are recorded together (simultaneously, i.e., in the same image) with the matrix-by-matrix capture of the object to be inspected. The line-by-line image information and the matrix-by-matrix image information, including the image information transmitted via the first deflecting mirrors, are assigned to the respective inspected object and included in the determination of the presence of a defect of at least one defect type and / or a quality indicator.Due to its technical features described above, the inspection device enables a quick and minimal assessment of the quality of various objects, especially pouch cells. In particular, only a single matrix camera is sufficient for the quality assessment of the top side of the object.

[0017] From the image information (image data) transmitted from the matrix camera to the data processing device, the presence of a defect of at least one defect type is determined through appropriate data processing and / or a quality indicator is determined, which allows an assessment of the quality of the object. Defect types include, for example, inclusions, craters (dents), protrusions (bumps), contamination (dust, electrolyte residues), pseudo-edges, orange peel, pores, cracking, grinding marks, specks, surface defects, blistering, scratches, and wet prints. This is explained in more detail below.

[0018] The arrangement and inclination of the at least one first deflecting mirror is designed such that the matrix camera receives the light reflected from the largest possible area of the respective lateral surface sections of the upper side. In one embodiment of the device, at least two, in particular four first deflecting mirrors are provided, wherein, when the object is at rest, each first deflecting mirror is arranged next to a side of the housing. With four first deflecting mirrors, the reflected light from the lateral surface sections of all sides of the housing can be recorded. In this case, each first deflecting mirror is designed, for example, such that its length (largest dimension, dimension parallel to the respective side next to which the first deflecting mirror is arranged) corresponds at least to the length of the respective side of the housing.Furthermore, in one embodiment, each first deflecting mirror is arranged at a distance of at least 30 mm from the respective side of the housing in the horizontal direction. In another embodiment, the width of each first deflecting mirror (dimension perpendicular to the respective side next to which the respective first deflecting mirror is arranged) is at least 20 mm. The tilt angle of the first deflecting mirror is, for example, at least 30° to the horizontal direction. Furthermore, it is advantageous for the accuracy of the inspection if the deflecting mirrors achieve very good optical imaging quality in order to avoid distortions in the image of the matrix camera.

[0019] In one embodiment of the device, the illuminated linear region extends over the entire length of the top side (optionally including the protruding first and second tabs). The length of the top side is the dimension of the housing in the direction of its greatest extent. In this embodiment, image information regarding the entire top side (and optionally both tabs) can be obtained using the illuminated linear region when the entire object is moved past the linear illumination device.

[0020] In one embodiment of the device, the surface illumination device is arranged to illuminate the entire upper side of the housing of the object in the resting state (optionally including the upper tab surface of the first tab and the second tab) of the object to be inspected in temporal succession from at least two different directions obliquely from above and the matrix camera is set up accordingly for the sequential matrix-wise acquisition of the image information when illuminated from at least two directions of the area illumination device and the data processing device is configured to receive and process the at least two items of image information acquired in a matrix manner when illuminated from at least two directions of the area illumination device, to assign this image information to the respective object and to use it to determine the presence of a defect of at least one defect type and / or the quality index which allows the assessment of the quality of the object.

[0021] In one embodiment of the device, the data processing device uses a maximum image, a topology image, and / or an absorption image of the image information to determine the presence of an error of at least one error type and / or the characteristic number. The maximum image, a topology image, and / or an absorption image of the image information was determined from at least two pieces of image information acquired in matrix form during illumination from at least two directions of the area illumination device. The matrix image, the topology image, and / or the absorption image are each generated from the n pieces of image information acquired in matrix form one after the other in a predetermined image information section. The maximum image represents the image information of the areas that are most easily accessible in relation to the respective lighting situation and are therefore displayed most brightly.The topology image has the advantage of highlighting topology changes in the image, whereas the absorption image accentuates defects caused by light absorption (e.g., dirt on the surface). For example, the image information is generated pixel-identically, i.e., the image information of the at least two matrix-wise acquisitions of the entire top surface (possibly including the upper tab surface of the first tab and the second tab) is each generated from the same locations on the surfaces. Each of these matrix-wise acquisitions is referred to as an image information matrix M, with at least two image information matrices Mk (k ≥ 2, k = 2 ... n) being acquired for each object. A pixel Pi of the acquired first image information matrix M1 thus corresponds to the same location on the top surface (possibly including the upper tab surface) as the same pixel Pi of the acquired second (third, fourth, etc.) image information matrix.) Image information matrix Mk (M2, M3, M4, ...Mn). The detected light intensity at pixel Pi is denoted as i(Pi). The detected light intensity of the first image information matrix M1 at pixel Pi is denoted as i1(Pi).

[0022] The maximum image can be determined by calculating the maximum of the light intensities of all image information matrices Mk in the respective pixel Pi, ie Max(i1(Pi), i2(Pi)) for two determined image information matrices M1, M2 for two illuminations from two different directions or Max(i1(Pi), i2(Pi), ... i n (Pi)) when n illuminations from n different directions are used. In one embodiment, n = 4. The maximum is calculated for each pixel Pi and results in the maximum image, represented in the entire matrix (maximum matrix).

[0023] The topology image and the absorption image can be determined by first applying two differently parameterized low-pass filters (e.g. box filters) independently to each image information matrix Mk of each lighting situation and subtracting them from each other: Fk=lowpass1(Mk)−lowpass2(Mk)

[0024] The parameters of the two low-pass filters lowpass1 and lowpass2 differ, for example, in such a way that the first parameter of the first low-pass filter lowpass1 is smaller than the second parameter of the second low-pass filter lowpass2. The light intensity assigned to each pixel Pi of the matrix Fk by this operation is denoted as fk(Pi) (k = 2 ... n).

[0025] Subsequently, a minimum matrix MinM and a maximum matrix MaxM are calculated from the resulting matrices Fk, analogous to the above maximum image, by pixel-by-pixel determination of the minimum and maximum, respectively, across all matrices Fk. Each point Pi of the minimum matrix MinM is calculated as Min(f1(Pi), f2(Pi), ... fn(Pi)) and each point Pi of the maximum matrix MaxM is calculated as Max(f1(Pi), f2(Pi), ... fn(Pi)). Subsequently, a matrix H with the values h(Pi) is determined, which is determined from the product - again determined pixel-by-pixel - of the minimum value and maximum value calculated at the respective point Pi with a scaling factor a (for example, a = 64). This means that for each point Pi, the value h(Pi)=Min(f1(Pi),f2(Pi),…fn(Pi))*Max(f1(Pi),f2(Pi),…fn(Pi))*a

[0026] From this, a matrix Q with the values q(Pi) is finally determined, where q(Pi)=sqrt(abs(h)Pi))), where abs(q(Pi)) is the absolute value of the value q(Pi) and sqrt() represents the root function. This results in the values of the topology matrix T with the values t(Pi) as follows: t(Pi)=q(Pi) if h(Pi)≤0 or t(Pi)=0 if h(Pi)>0.

[0027] Accordingly, the values of the absorption matrix A with the values a(Pi) are as follows: a(Pi)=q(Pi)if h(Pi)>0 or a(Pi)=0 if h(Pi)≤0.

[0028] The topology matrix T calculated in this way with the values t(Pi) is also called the topology image and the absorption matrix A with the values a(Pi) is also called the absorption image.

[0029] If the matrix-wise recording of the light of the area illumination device reflected upwards from the entire upper side (possibly including the upper tab surface) is carried out four times with illumination from four different directions obliquely from above, the directions are selected, for example, so that the illumination occurs from both opposite long sides and from both opposite short sides of the housing. Alternatively, the illumination can strike the upper side from the direction of the four corners of the housing. In all cases, it is advantageous if the images are taken with illumination that, with regard to the component running in the plane of the upper surface section, covers an angle of 360° as far as possible (i.e.when lighting from four different directions, the lighting is provided from directions offset by 90°, or when lighting from six different directions, the lighting is provided from directions offset by 60°, etc.).

[0030] In one embodiment of the device, the matrix camera is calibrated such that the data processing device can take perspective and optical distortion from the image information acquired in the matrix into account. For such a calibration, the method described in the article "Digital Camera Self-Calibration," C.S. Fraser, ISPRS Journal of Photogrammetry & Remote Sensing 52 (1997), pages 149-159, is used, for example.

[0031] In one embodiment of the device, the data processing device is configured to determine at least one dimension of the object and / or at least one size of a detected error after taking perspective and optical distortion into account. For this purpose, a look-up table is determined in advance based on the calibration, for example, which allows a conversion of a pixel number into a unit of length or area. The look-up table is stored, for example, in a memory unit of the data processing device.

[0032] In one embodiment, a position correction can also be carried out by the data processing device based on the calibration and by using fixed points (for example, the corners of the housing). In this case, the coordinates of the four corners of the housing, for example, are determined by software-based "probing" of the housing in the horizontal and vertical directions. The probing involves examining the respective rows and columns of the image information matrix for a jump in intensity (a large increase or decrease in intensity from one pixel to the next pixel). This is advantageous for comparing the acquired image information of the matrix with corresponding target values to identify errors or to determine a quality indicator, since the object cannot always be positioned exactly in the same position when at rest.In one embodiment, the position correction can also be used to determine the location (position, location) of each detected defect on the upper side (possibly including the upper tab surface). Based on this location information, a marking device downstream of the inspection device can, for example, mark the defect by applying (e.g., spraying) a water-soluble paint by circling the surface of the object. Alternatively or additionally, knowledge of the defect location can facilitate the control of a defect removal device.

[0033] The above object is also achieved by a system having a first inspection device having the features described above and a second inspection device having the features described above, wherein the second inspection device is arranged behind the first inspection device in the transport direction, wherein the underside of the object is inspected by means of the second inspection device, which underside is on top after the object has been turned over after the first inspection device. The inspection of the underside of the object is preferably carried out analogously to the inspection of the top side of the object. For example, a turning device can be arranged between the first inspection device and the second inspection device for turning over, which turns the object over in such a way that the underside is on top for inspection in the second inspection device.The system enables the detection of defects and / or the determination of a quality indicator both on the top side and, after passing the turning device, on the bottom side of the object (including the lower tab surface if necessary). For example, the turning device is implemented by means of grippers and / or suction cups.

[0034] The above object is also achieved by a method for inspecting 3-dimensional objects, in particular pouch cells, wherein each object has a substantially pillow-shaped or cuboid-shaped housing with an upper side and a lower side opposite the upper side, wherein the upper side of the housing is composed of at least one upper surface section and a plurality of lateral surface sections which are inclined or perpendicular to the at least one upper surface section or which represent corner sections, wherein the underside of the housing is composed of at least one lower surface section on the underside and a plurality of lateral surface sections which are oblique or perpendicular to the at least one lower surface section or which represent corner sections, the method comprising the following steps: • Recording movement information of a relative movement of each object to be inspected to a line illumination device (movement state) and an arrangement of the respective object in a predetermined position and over a predetermined period of time with respect to an area illumination device (rest state) by means of a movement detection device, • Illuminating a linear area of the top of the object to be inspected using the line illumination device, which, for example, emits a linear HDR reflection bright field illumination, • Illuminating the entire top of the housing of the object to be inspected from above in its resting state using the surface lighting device, • Acquisition of image information in a field of view by means of a matrix camera arranged above the rest position of the object to be inspected, whereby the field of view ◯ for line-by-line detection of the light of the line illumination device reflected from the line-shaped area of the upper side in the moving state of an object to be inspected and ◯ for the matrix-wise detection of the light of the surface illumination device reflected upwards from the entire upper side in the rest state of the object to be inspected, including the light reflected from the lateral surface sections of the object to be inspected, optionally via the at least one first deflection mirror into the matrix camera, wherein the at least one first deflection mirror is arranged next to one side of the housing in the rest state of the object, • Receiving and processing the image information recorded by the matrix camera by means of a data processing device, wherein the data processing device assigns the image information recorded line by line in the moving state and the image information recorded matrix by matrix in the idle state to the respective object to be inspected and determines from this image information the presence of an error of at least one error type and / or determines a quality index which allows an assessment of the quality of the object.

[0035] In one embodiment of the method, the illuminated linear region extends over the entire length of the upper side and / or at least four first deflecting mirrors are provided, wherein each first deflecting mirror is arranged next to one side of the housing in the rest state of the object.

[0036] In one embodiment of the method, the matrix camera, at least in one of its recordings of a capture sequence • line by line the light of the line illumination device reflected from the line-shaped area of the top side in the moving state of a first object and • the light of the surface illumination device reflected upwards from the entire top side in the rest state of a second object and the light reflected from the lateral surface sections of the second object, optionally via the at least one first deflection mirror into the matrix camera, wherein the second object is different from the first object (ie in the same recording) are recorded in a matrix manner.

[0037] In one embodiment of the method, the surface illumination device illuminates the entire top side of the housing of the object in the resting state of the object to be inspected in succession from at least two different directions obliquely from above and the matrix camera, the image information of the matrix camera for the illumination from at least two directions of the area illumination device is recorded matrix-wise in a timely manner and by means of the data processing device, the at least two items of image information acquired in a matrix manner are received and processed accordingly during illumination from the at least two directions of the area illumination device, this image information is assigned to the respective object and used to determine the presence of an error of at least one error type and / or the quality index, wherein the data processing device preferably uses a maximum image of the image information for determining the presence of an error of at least one error type and / or the index, which was determined from the at least two items of image information acquired in a matrix manner during illumination from the at least two directions of the area illumination device.

[0038] In one embodiment of the method, the matrix camera is calibrated such that perspective and optical distortion from the image information acquired in a matrix manner can be taken into account by the data processing device, wherein at least one dimension of the object and / or at least one size of a determined error is preferably determined by means of the data processing device after taking the perspective and the optical distortion into account.

[0039] The inspection device may comprise further lighting devices and / or cameras that illuminate predetermined particular sections of the surface of the housing or generate image information from these sections that is used to inspect the objects.

[0040] The inspection method for the object can be implemented as a computer-implemented method, i.e., a method performed using the data processing device (computer), based on the acquired image information. The method can also include controlling the line illumination unit and / or the area illumination unit and / or the matrix camera such that a predetermined detection and / or illumination sequence is implemented. For this purpose, the data processing device and the line illumination unit and / or the area illumination unit are connected to one another by wire or wirelessly. The matrix camera is also connected to the data processing device by wire or wirelessly, also for transmitting the image information acquired by the matrix camera to the data processing device.

[0041] The data processing unit for processing the image information and determining whether an error of at least one error type is present and / or determining which quality index can be assigned to the object comprises a processor, which represents a functional module that interprets and executes instructions / commands from algorithms, as well as a command control unit, an arithmetic unit, and a logic unit. The processor can comprise at least a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA - a digital integrated circuit into which a logic circuit can be programmed), a discrete logic circuit, and any combination of these components. The data processing unit can also comprise a memory unit, an input module (e.g., keyboard or touchpad), a power supply module (e.g.,battery) and a display module (e.g. display). The data processing unit can be embodied as a real hardware resource, for example a smartphone, desktop computer, server, notebook, cluster / warehouse-scale computer, embedded system or the like, or as a virtualized computer resource. Furthermore, the data processing unit can have a transmitter / receiver (transceiver) for exchanging data / image information with a display device. The data processing unit also has an interface for exchanging data with the line lighting device and / or the area lighting device and / or the matrix camera and / or a control device for the movement device.

[0042] As already explained above, the method explained above can be implemented, for example, as a computer program or computer-implemented method comprising instructions which, when executed, cause a processor of the data processing unit to carry out the steps of the above method, wherein the computer program comprises a combination of the steps described above and data definitions which enable the computer hardware to carry out computing or control functions, and / or which represents a syntactical unit which conforms to the rules of a specific programming language and which consists of declarations and statements or instructions which are required for the functions, tasks or problem solutions explained above.

[0043] Furthermore, a computer program product is disclosed comprising instructions that, when executed by the processor of the data processing unit, cause the device to perform the steps of one or all of the methods defined above. Accordingly, a computer-readable medium storing such a computer program product is disclosed. The computer program product may be a software routine.

[0044] Further advantages, features, and possible applications of the invention are described below with reference to exemplary embodiments and the figures. All described and / or illustrated features form the subject matter of the present invention, regardless of their summary in the claims and their references.

[0045] They show schematically: Fig. 1 an embodiment of a device according to the invention in a first perspective view from the side, Fig. 2 the embodiment according to Fig. 1 in a second perspective view from the side, Fig. 3 the embodiment according to Fig. 1 in a side view with the marginal rays of lighting devices and the field of view of the matrix camera, Fig. 4 the embodiment according to Fig. 1 in a front view with the marginal rays of an illumination device and a central beam of the field of view of the matrix camera, Fig. 5 the embodiment according to Fig. 1 in a front view with the marginal rays of lighting devices and the marginal rays of the field of view of the matrix camera, Fig. 6 a lower section of the embodiment according to Fig. 1 with the field of view of the matrix camera in a top view, Fig. 7 an embodiment of a system according to the invention in a perspective view from the side, Fig. 8 a first example of the design of an object (pouch cell) in a perspective view from the side and Fig. 9 a second example of the design of an object (pouch cell) in a perspective view from the side and Fig. 10 an embodiment of a method for inspection as a flow chart.

[0046] The Fig. The embodiment of an inspection device shown in Figures 1 to 6 is used to inspect objects, e.g. in the form of pouch cells.

[0047] Two examples of pouch cells 11, 111 are shown in the Fig. 8 and Fig. 9 shown.

[0048] The pouch cell 11 (see Fig. 8) has a substantially pillow-shaped housing 12. The housing 12 includes a first terminal tab (tab) 14 projecting from one short side and a second terminal tab (tab) 15 projecting from the opposite second short side. The housing 12 has a top with a substantially horizontally extending upper surface portion 13. The first tab 14 has an upper tab surface 17 and the second tab 15 has a corresponding upper tab surface 18. In Fig. 8 not visible are the corresponding lower tab surfaces of the tabs 14, 15. The housing 12 is essentially cuboid-shaped. On the short sides, the housing 12 has side surfaces 21, 22 and on the long sides, side surfaces 23, 24. The side surfaces 21, 22, 23, 24 run approximately perpendicular to the horizontal upper surface section 13. The horizontal upper surface section 13, the upper tab surfaces 17, 18 of the tabs 14, 15 and the side surfaces 21, 22, 23, 24 together form the upper side of the housing 12. The underside is designed accordingly and has a horizontal lower surface section, lower tab surfaces of the tabs 14, 15 and the side surfaces 21, 22, 23, 24. In this embodiment, the side surfaces 21, 22, 23, 24 belong to both the top and the bottom, since they can also be detected during the inspection of the top or bottom. Fig. 8, the length of the housing 12 is designated L and the width B (see dashed double arrow lines). Due to the simple design of the pouch cell 11, it is used to explain the operation of the inspection device 1 (see Fig. 1 to 6). However, the inspection device 1 can also be used for other types of objects, particularly in the form of pouch cells.

[0049] Fig. Figure 9 shows a second embodiment of a pouch cell 111, which has a pillow-shaped housing 112 with a horizontal upper surface portion 113. The housing also includes a first terminal tab (tab) 114 and a second terminal tab (tab) 115, which are arranged side by side and protrude from a single short side 112. The first tab 114 has an upper tab surface 117, and the second tab 115 has an upper tab surface 118.

[0050] The pouch cell 111 further comprises, on the housing 112, side edges 121, 122, 123, 124 around the horizontal upper surface section 113, which extend obliquely to the horizontal upper surface section 113 and merge into it with a curve. Corners 125 are formed at the transition from one side edge to the adjacent side edge 121, 122, 123, 124. Furthermore, the housing 112 comprises terrace sections 126, 127, 128, 129, which each adjoin the side edges 121, 122, 123, 124 and run substantially parallel to the horizontal upper surface section 113. The top of the housing 112 is formed by the horizontal upper surface section 113, the side edges 121, 122, 123, 124, the corners 125 and the terrace sections 126, 127, 128, 129.

[0051] The Fig. The device 1 shown in Figures 1 to 6 for inspecting the pouch cell 11 has a frame 3 on which a base plate 5 is arranged, which has a first through opening 7 and a second through opening 8 (cf. Fig. 1, Fig. 2 and Fig. 6). From a large number of pouch cells to be inspected, Fig. 1 to 6, two pouch cells 11, 31 to be inspected are shown, which are guided past the inspection device 1 below the base plate 15, as illustrated by the arrows 11a and 31a. The pouch cell 31 is another pouch cell with a structure as in Fig. 8 shown.

[0052] Above the base plate 5, a matrix camera 40 is arranged on the frame 3, which looks from above through the openings 7, 8 onto the pouch cells 11, 31. The pouch cells 11, 31 are arranged such that the upper side of the housing 12 is at the top and the horizontal upper surface section 13 can be viewed from above with the matrix camera 40. The upper tab surface 17 of the first tab 14 and the upper tab surface 18 of the second tab 15 are also captured by the matrix camera 40. The field of view 42 of the matrix camera 40 is so large (see in particular Fig. 6) that it extends over both openings 7, 8 in such a way that both the pouch cell 11, 31 arranged below the respective opening 7, 8 in its entire length and width (viewed from above) and the deflection mirrors 65, 67 arranged next to the pouch cell 11 are also detected.

[0053] Furthermore, a line illumination device 51 is provided on the frame 3, which illuminates a line-shaped area 31b of the upper side of the housing and the upper tab surfaces of the tabs. As can be seen from Fig. 4, the light reflected from the top side of the housing (including the upper tab surface) reaches the matrix camera 40 via the viewing beam 41 and is recorded there. The matrix camera 40 thus records the illuminated linear area 31b of the pouch cell 31 line by line, with the pouch cell 31 moving transversely to the length of the opening 8 (see arrow 31A in Fig. 6) is moving, i.e., is in a moving state. Thus, a plurality of images are generated by the matrix camera 40. Each image contains an image of the illuminated linear region of the top side of the housing (including the upper tab surfaces) of the passing pouch cell 31. The pouch cell is moved in a moving state by means of a movement device described in more detail below, brought into a rest state for a predetermined period of time, and moved out of the inspection device from the rest state.

[0054] In addition, four surface lighting devices 52, 53, 55, 56 are provided on the frame. As can be seen from the Fig. 2, Fig. 3, Fig. 5 and Fig. 6 and the edge rays 52a and 52b or 53a, 53b of the area illumination devices 52, 53 show, the area illumination devices 52, 53 illuminate the upper side of the housing 12 (including the upper tab surface 17, 18 of the tabs 14, 15) of the pouch cell 11 over the entire length L from obliquely above, so that in particular the side opposite the respective area illumination device 52, 53 with respect to the width of the pouch cell 11 (compare in particular Fig. 5) which is arranged below the opening 7 in the base plate 5. As can be seen Fig. 3, the surface illumination devices 55, 56 illuminate the upper side of the housing 12 (including the upper tab surface 17, 18 of the tabs 14, 15) via the mirrors 61, 62. The light emitted by the surface illumination device 55 falls via the mirror 62 essentially onto the opposite first end of the pouch cell 11, and the light emitted by the surface illumination device 56 via the mirror 61 falls essentially onto the second end of the pouch cell 11 opposite the first end of the pouch cell (in the longitudinal direction). This can be reproduced by the edge rays 55a, 55b, 56a, and 56b. The surface illumination devices 52, 53, 55, 56 also partially illuminate the side surfaces 21, 22, 23, 24, so that reflections from these side surfaces and from the horizontally extending upper surface section 13 of the housing 12 are captured by the matrix camera 40.The light of the area illumination devices 52, 53, 55, 56 reflected by the side surfaces 21, 22, 23, 24 is detected in particular by means of the deflecting mirrors 65, 67, which are provided next to the pouch cell 11 arranged below the opening 7 in such a way that the side surfaces 23, 24 on the long sides are viewed by means of the deflecting mirrors 65, while the deflecting mirrors 67 serve to detect the side surfaces 21, 22 on the short sides of the housing 12 of the pouch cell 11.

[0055] The illumination by means of the area illumination devices 52, 53, 55, 56 is now carried out in such a way that these are switched on individually one after the other, so that the pouch cell 11 is illuminated obliquely from above, while the other three area illumination devices are switched off. For example, the illumination is first provided by the area illumination device 52, then by the area illumination device 55, then by the area illumination device 53, and finally by the area illumination device 56. The matrix camera 40 captures the reflected light in each of the four illumination states, with the pouch cell 11 being in the rest state, i.e., at the same, predetermined position below the opening 7 in the base plate 5, in all four illumination states.Accordingly, four images of the entire upper side of the housing 12 (including the upper tab surface 17, 18) are generated by means of the matrix camera 40, which capture these areas four times in a matrix, namely once each with the area illumination device 52, area illumination device 55, area illumination device 53 and area illumination device 56 switched on, wherein the pouch cell 11 is in the same position in each case.

[0056] In the 4 matrix-wise detection of the entire upper side of the housing 12 (including the upper tab surface 17, 18), the side surfaces 21, 22, 23, 24 are also detected via the deflection mirrors 65, 67, because the light reflected from these side surfaces 21, 22, 23, 24 reaches the matrix camera 40 via the deflection mirrors 65, 67, because the field of view 42 of the matrix camera 40 includes these areas, as in Fig. 6 shown.

[0057] The plurality of line-by-line recordings and matrix-by-matrix recordings of the pouch cell 11, 31 by the matrix camera 40 are transmitted to the data processing device (computer) 70 (see Fig. 1). The data processing device 70 receives the image information from the line-by-line and matrix-by-matrix acquisition of the respective pouch cell 11, 31.

[0058] Here, the movement state and the rest state of the pouch cells 11, 31 are recorded by a movement detection device of the inspection device. For example, by monitoring the movement of a predetermined marking on the pouch cell 11, 31, e.g., a barcode, or corresponding signals from the movement device, movement information is generated, which in particular contains information about the respective movement state in which the respective pouch cell 11, 31 is located. For example, the movement device can transmit to the movement detection device the information that a pouch cell is ready for inspection (start signal). From this point on, the movement detection device can continuously record the movement information of the movement device and thus of the respective pouch cell (e.g., the movement speed of the movement device).Alternatively or additionally, the motion detection device receives a signal when the line-by-line acquisition of the respective pouch cell is complete. A signal is then generated by the motion device or based on a marker on the pouch cell and transmitted to the motion detection device when the respective pouch cell is in the specified idle position and remains there motionless. After the matrix-by-matrix acquisition of the respective pouch cell is complete, the motion detection device generates another signal indicating that the respective pouch cell can be transported out of the inspection device. These signals also represent important motion information required for processing the image information.

[0059] This image information and the movement information are further processed and evaluated by the data processing device 70. From this, as explained in more detail below, the object is assessed with regard to the presence of a defect of at least one defect type and / or a quality index is determined, which allows an assessment of the quality of the pouch cell 11, 31. The image information determined at different times from the line-by-line acquisition and the matrix-by-matrix acquisition of the respective pouch cell 11, 31 is assigned to the movement state and the rest state. This can be done, for example, based on the movement information transmitted by the movement detection device regarding the movement state and rest state of the respective pouch cell 11, 31.The image information of the generated images is corrected with regard to image cropping, mirror distortion, line-by-line acquisition (so-called line scan correction) and the position associated with the respective state of movement or rest.

[0060] From the image information of the line-by-line recording of the respective pouch cell 11, 31, for example, defects can be searched for using bright field illumination (Reflection Bright Field (RBF) illumination) or a quality indicator can be determined, which relates in particular to an evaluation of the tab surfaces and the contamination by electrolyte.

[0061] For example, as explained in more detail below, the data processing device 70 generates a quality statement (quality indicator) from the number, error type and size / dimension of detected errors.

[0062] Additionally, additional cameras 45, 46 are arranged on the frame 3 and are attached to the frame 3 below the matrix camera. They view the top side of the pouch cell 11 from above (see edge rays 45a, 45b, 46a, 46b) in such a way that they observe an upper tab surface 17 of the first tab 14 and an upper tab surface 18 of the second tab 15, as well as an adjacent section of the upper surface section of the top side 13. The additional cameras 45, 46 generate images with a higher resolution in the specified sections of the pouch cell 11. The image information obtained from the corresponding viewing areas is transmitted to the data processing device 70, and from this, further information regarding minor defects is generated.

[0063] Fig. 7 shows a system according to the invention for inspecting a pouch cell. The top side of the pouch cell (e.g., pouch cell 11) is first inspected by means of the inspection device 1. The pouch cell (e.g., pouch cell 11) then reaches the turning device 180 with a rotating device and grippers with suction cups, which turns the pouch cell (e.g., pouch cell 11) so that the underside is now on top. The pouch cell (e.g., pouch cell 11) is then inspected by means of an inspection device 101, which is identical in construction to the inspection device 1, specifically its underside (which is on top there). The processing of the image data obtained by means of the inspection devices 1, 101 with regard to the pouch cell (e.g.,Pouch cell 11) and the determination generated from this image information as to whether a defect / multiple defects of at least one defect type are present and / or the determination of a quality indicator that allows an assessment of the quality of the object is carried out by means of the data processing device 170, which is connected to a display 172 for displaying the results of the inspection. The pouch cell (e.g., pouch cell 11) is transported from the first inspection device 1 to the turning device 180 and to the second inspection device 101 by means of a movement device that, for example, comprises carriages that are displaceable on a linear unit. The respective pouch cell is attached to a carriage by means of suction cups.After the acquisition of the image information by the matrix camera 40 and, if applicable, by the additional cameras 45, 46, in particular after the completion of the matrix-wise acquisition of the respective pouch cell, a corresponding signal is generated by the inspection device, which is transmitted to the control of the movement device. The movement device is then controlled such that it conveys the respective pouch cell out of the respective inspection device 1, 101 and, if applicable, transports it further to the turning device 180, to be subsequently turned and then transported further to the second inspection device 101.

[0064] The evaluation of the captured image information to determine the presence of an error and / or to determine a quality indicator can be carried out, for example, as follows. The procedure is described using the flow chart shown in Fig. 10 is shown.

[0065] The starting point for the evaluation of the acquired image information is the four image information 200 of the top side of the object, acquired in matrix form by illumination from different illumination directions, as well as the image information 201 of the top side of the object, acquired in rows, using the pouch cell 101 as an example.

[0066] As described above, for each of the four pieces of image information acquired in a matrix, the perspective and / or optical distortion of the matrix camera is first compensated in a step 202. Subsequently, in a step 204 (position correction), the actual position of the object as it was optically acquired by the matrix camera is corrected, if necessary, i.e., rotated and / or shifted, so that the image data assume a predetermined position of the object in the field of view of the matrix camera. In parallel, in step 203, the data processing device 70 corrects the line-by-line image information for inconsistencies in composing the image of the top side of the object from the line-by-line image information, as described above. Subsequently, in step 204, this data is also corrected with regard to its position as described above.The combined and corrected line-by-line image information forms the second overall matrix.

[0067] For the four image information items acquired, compensated, and corrected in a matrix, the determination of a maximum image, an absorption image, and / or a topology image of the top surface of the object follows in step 206. The calculation from the four matrices of image information is described in detail above. The maximum image is also referred to as the first overall matrix.

[0068] Segmentation is then performed in step 210. Layout recipes, for example, can be used for segmentation to extract desired image information sections from the respective (corrected) image information matrix of the top side determined by matrix-by-matrix or line-by-line acquisition.

[0069] Basically, when segmenting using a layout recipe, the layout recipe defines a predefined section of the object with respect to the field of view of the matrix camera. Since the object is not always exactly in the specified ideal position when the image is captured by the matrix camera, but may be shifted / rotated by a few pixels, a position correction is performed, for example, based on predefined fixed points of the object. This means that a registration to the expected position is performed, so that the first overall matrix (or the corresponding n first overall matrices or the second overall matrix determined from the row-by-row view) is adjusted accordingly to the ideal position of the object. The aforementioned matrices with image information are rotated and / or shifted accordingly.Once this adjustment has been made, the desired image information sections can be reliably identified and extracted accordingly using the given layout recipe.

[0070] For example, a first image information section is extracted in the form of the upper surface section (image information from direct recording by the camera / matrix camera) 113, a second image information section is extracted from the recording areas via the mirrors on the shorter side in the form of the two corner sections 125, and a further second image information section is extracted in the form of the four terrace sections (image information from direct recording by the camera / matrix camera) 126, 127, 128, 129. Segmentation is performed both with regard to the corrected and combined image information acquired line by line and with regard to the matrix-wise acquired, compensated, and corrected image data, as well as the maximum image and / or the absorption image and / or the topology image. The image information sections obtained through segmentation are then processed in parallel and finally fed into an overall evaluation of the object.

[0071] The result of the segmentation in step 230 is, for example, an image information section of the upper surface in the maximum image, the absorption image, the topology image, and the second overall matrix. In step 232, the data processing device 70 examines, for each of these image information sections, with respect to each pixel, whether these exceed a predetermined threshold value. Such a threshold value could be 240 for the image information section of the maximum image, 220 for the image information section of the topology image, 203 for the image information section of the absorption image, and 120 for the image information section of the second overall matrix. If the image information value of the respective pixel is at or above the respective threshold value, an error is detected.Subsequently, in step 234, further properties of the detected error are determined, for example, its size (by analyzing whether an error was also detected in neighboring pixels), a histogram of the image information values in the area of the respective error, and / or the shape and orientation of the error. Based on the properties of the detected error and, if applicable, other errors found in the image information section, the error type is then determined in step 236. Different images / matrices relating to the same location in the image information section can be used for this purpose.For example, based on the absence of a defect at the location in the absorption image, based on the determined length-to-width value in the absorption image being greater than 5, and based on an average value of an image information histogram along the defect in the topology image being greater than 200, the overall defect type can be determined to be "scratch." In step 238, the severity of the detected scratch defect is then determined, wherein the determination can be based, for example, on an assignment of the size of the scratch to severity classes. If the defect is less than 2 pixels, the scratch defect can be assigned severity zero; if the defect is greater than or equal to 2 pixels and less than 4 pixels, the scratch defect can be assigned severity 1; if the defect is greater than or equal to 4 pixels and less than 6 pixels, the scratch defect can be assigned severity 2; and so on.

[0072] The result of the segmentation in step 240 is, for example, image information sections in the form of four corner sections (e.g., 128 x 128 pixels) from the maximum image determined in step 206, wherein the corner sections are obtained, for example, from the image information generated via the mirrors 67 on the short side of the pouch. A CNN algorithm with a binary classifier, explained below, is then applied to each of these image information sections in step 242. As a result, the attribute "defective corner" (step 244) or "intact corner" (step 246) is determined for each corner image information section and assigned to the respective corner in step 248.

[0073] The corner image information matrix of each corner section can be analyzed using a convolutional neural network (CNN) algorithm that includes a binary classifier (a classifier with two states, namely "intact corner" and "defective corner"). The corner image information matrix is comparatively small (e.g., 128 x 128 pixels) and only contains the area of the corner of the housing. This CNN model was specifically developed for this classification task with few classes and for matrices with few pixels. This CNN model has a compact structure that is, for example, more compact than conventional deep architectures. It consists of three threads with different convolution sizes, which are later merged. This structure reduces the number of parameters to be trained, so the model can learn fewer features.For this reason, it is ideally suited for binary or other low-dimensional classification. A large dataset containing matrices of corner structures and corresponding expected errors is used to train and evaluate the CNN model. This dataset is compiled with respect to the respective object under investigation and annotated by engineers. This dataset contains images of the corners of the respective objects, divided into two classes: "defective" and "intact." The images were carefully selected and annotated to ensure they cover a wide range of defects and variations in the battery corners. The model was trained using the dataset, with the images in the dataset being divided into training sets and validation sets. For example, a ratio of 80% for the training data and 20% for the validation data can be used.Additionally, five-fold cross-validation can be performed to ensure that all images are present in both the training and validation data. The model consists of multiple convolutional layers, pooling layers, and fully connected layers, which enable the model to extract important features from the images and detect subtle differences between defective and intact corners. The convolutional layers are used to train the trainable weights of the convolutional operations, which are then used to detect image features. These features are then aggregated with the pooling layers. The weights of the fully connected layers are then iteratively trained to determine a probability for the corresponding class from the features.In addition, before analysis with the CNN algorithm, all images / sections were cropped to the same size as the corner image information matrices to be analyzed (128x128 pixels) and aligned in such a way that they were brought into the same orientation to enable a consistent view.

[0074] The result of the segmentation in step 250 is, for example, image information sections in the form of terrace sections (regarding terraces 126, 127, 128, 129) from the maximum image determined in step 206, wherein the image information was generated by direct recording from above using the matrix camera 40. The Mask-RCNN algorithm is then applied to these terrace image information sections (described in more detail below, step 252). As a result, errors of various error types are detected in the terrace image information sections and assigned a bounding box (step 254). The severity of the respective error is then determined with respect to the terrace image information sections, for example, based on the detected error type, the size of the bounding box, the shape of the bounding box, etc. (step 256).

[0075] Here, each region of the predefined areas of the lateral surface sections extracted by segmentation (e.g., terrace sections of a pouch cell) is analyzed using an object detection network based on the Mask-RCNN model. The algorithm detects defects of a variety of different defect types (e.g., six different defect types such as protrusion / nose / bulge, notch, fold, scratch, contamination, and particles) and supplements the data of the corresponding second image information section in the defect area with a corresponding border (bounding box). To train the Mask-RCNN model as an algorithm, data representing a corresponding second image information section is used. In these image information matrices, the corresponding defects are annotated and provided with a bounding box. The architecture of the Mask-RCNN model was carefully selected.This model represents a further development of the Faster R-CNN model and is capable of generating bounding boxes and masks for defects in specified regions. Rarely occurring defect types are artificially inserted into corresponding image information sections of the specified regions to train the model. The performance of each model is evaluated using a separate validation dataset. For example, a validation dataset with a ratio of 80% for training data and 20% for validation data can be used. This ensures that the model efficiently and accurately detects and correctly classifies defects.

[0076] The result of the segmentation in step 260 is, for example, image information sections in the form of the upper surface section 113, for example in the maximum image and in an image composed of the line-by-line acquisition. These image information sections are divided into patches in step 262 and then analyzed in step 264 as described below using the pre-trained CNN algorithm "Wide ResNet-50," and if necessary, one or more anomalies are detected in some patches. In step 266, the Mahalanobis distance to the normal distribution is determined for each patch in which an anomaly was detected and for each detected anomaly. From this, the severity of the anomaly and thus of the respective error is determined in step 268.

[0077] When analyzing the segmentation result in step 260, in one embodiment, the resolution in the specified area can be reduced to a specified value (e.g., from 5120 x 2216 to 841 x 265 pixels) to speed up the process. The image is divided into several small patches (sub-areas) (step 262). A pre-trained CNN algorithm, Wide ResNet-50, is then used as the first NN algorithm to examine each patch to determine whether one or more specified features are present in the respective patch. In Wide ResNet-50, the layers of the network are made "wider" by increasing the number of channels in the convolutional layers. This CNN is capable of recognizing complex patterns and textures. It has also been observed that such wider CNNs can often generalize better, meaning they can process new, unknown data more effectively.The method, also known as PaDiM (Patch Distribution Modeling Framework for Anomaly Detection and Localization), is an algorithm for the task of anomaly detection and localization. This approach is particularly suitable for industrial defect detection, where the goal is to identify irregularities or deviations from the norm in visual data. PaDiM models the distribution of an image's features. Then, for each patch, the features extracted by the CNN are collected. For each patch, the Mahalanobis distance between the patch's features and a normal distribution derived from the training data is calculated. This step determines how 'abnormal' or unusual each patch is compared to normal training data. The calculated Mahalanobis distance serves as the anomaly score, with a higher value indicating a greater deviation from normality.Based on the anomaly score, a threshold is set. Patches with a score exceeding this threshold are considered abnormal. Anomalies are localized by marking the positions of the patches classified as abnormal in the image information section, which allows the anomalies to be localized in the respective image information section.

[0078] The anomaly score is calculated separately for each patch by calculating the Mahalanobis distance of its features from the expected normal distribution, represented by the mean and covariance matrix from the training data. A large Mahalanobis distance indicates that the patch's features deviate significantly from the normal distribution, indicating a potential anomaly. Mathematically, the Mahalanobis distance D of a point x from a distribution with mean µ and covariance matrix Σ is calculated as follows: D(x)=(x−μ)⊤∑−1(x−μ) • D(x) is the Mahalanobis distance for the point • X is the vector of observed values. • µ is the mean vector based on the training data set. • Σ is the covariance matrix of the training data. • S¯ 1 is the inverse of the covariance matrix. • T denotes the transposition of the vector.

[0079] The anomaly score of each patch results in an assessment of the severity of the error present in the patch.

[0080] In all the above cases, the severity of the error is expressed in the form of predefined classes.

[0081] Subsequently, in step 270, the data processing device 70 evaluates the quality of the pouch cell 111 overall based on the defects identified in the four analysis strands, the respective defect type, and the respective severity of the defect. It is evaluated whether the pouch cell 111 overall meets the specified quality requirements or not. In step 280, the result of the overall evaluation is transmitted to an interface of the data processing device, optionally together with a list of the detected defects and their properties. For example, a pouch cell with two defects of the "Dent" type of severity class 5 is assessed as sufficient for the quality requirements. In contrast, a defect of the "Dent" type of severity class 7, for example, can be classified as not meeting the quality requirements.

[0082] The above procedure can also be carried out analogously for the bottom side of the pouch cell.

[0083] As shown above, the method according to the invention allows an inspection of a three-dimensional object, e.g. a pouch cell, to be carried out in a simple and rapid manner, the analysis taking into account in particular the various properties of the sections of the object.

Claims

[1] Device (1, 101) for inspecting 3-dimensional objects, for example pouch cells (11, 31, 111), each object having a substantially pillow-shaped or cuboid-shaped housing with a top side and a bottom side, wherein the upper side of the housing (12, 112) is composed of at least one upper surface section (13, 113) and a plurality of lateral surface sections (17, 18, 21, 22, 23, 24, 117, 118, 121, 122, 123, 124, 125, 126, 127, 128, 129) which run obliquely, parallel or perpendicular to the at least one upper surface section (13, 113) or represent corner sections (125), wherein the underside of the housing is composed of at least one lower surface section and a plurality of lateral surface sections which are inclined, parallel or perpendicular to the at least one lower surface section or which represent corner sections, wherein the device • a motion detection device which, with respect to each object to be inspected, detects movement information with respect to a relative movement to a line illumination device (movement state) and with respect to an arrangement in a predetermined position and over a predetermined period of time with respect to an area illumination device (rest state), • the line illumination device (51) for illuminating a line-shaped area of the upper side of the object to be inspected, • the surface illumination device (52, 53, 55, 56) for illuminating the entire upper side of the housing of the object to be inspected from above at an angle in its rest state, • optionally at least one first deflection mirror (65, 67) arranged next to each side of the housing in the rest state of the object and • a matrix camera (40) arranged above the rest position of the object to be inspected for capturing image information in a field of view, wherein the field of view ◯ for line-by-line detection of the light of the line illumination device reflected from the line-shaped area of the upper side in the moving state of the object to be inspected and ◯ is designed for the matrix-wise detection of the light of the surface illumination device reflected upwards from the entire upper side in the rest state of the object to be inspected, including the light reflected from the lateral surface sections, optionally via the at least one first deflection mirror into the matrix camera, and wherein a data processing device (70, 170) is provided which is configured to receive and process the image information recorded by the matrix camera and the recorded movement information, wherein the data processing device assigns the image information recorded line by line in the moving state and the image information recorded matrix by matrix in the idle state to the respective object to be inspected and determines from this image information the presence of a defect of at least one defect type and / or determines a quality index which allows an assessment of the quality of the object. [2] Device according to claim 1, characterized by that the illuminated linear area extends over the entire length of the top. [3] Device according to one of the preceding claims, characterized bythat the matrix camera is set up in such a way that at least in one of its recordings of a capture sequence • line by line the light of the line illumination device reflected from the line-shaped area of the top side in the moving state of a first object and • the light of the surface illumination device reflected upwards from the entire upper side in the rest state of a second object and the light reflected from the lateral surface sections of the second object into the matrix camera via the at least one first deflection mirror, wherein the second object is different from the first object, are recorded in a matrix manner. [4] Device according to one of the preceding claims, characterized by that at least four first deflecting mirrors are provided, wherein in the rest state of the object each first deflecting mirror is arranged next to one side of the housing. [5] Device according to one of the preceding claims, characterized by that the surface illumination device is arranged to illuminate the entire upper side of the housing of the object in the resting state of the object to be inspected in succession from at least two different directions obliquely from above and that the matrix camera is set up accordingly for the sequential matrix-wise capture of the image information when illuminated from at least two directions of the area illumination device and the data processing device is configured to receive and process the at least two items of image information acquired in a matrix manner when illuminated from at least two directions of the area illumination device, to assign this image information to the respective object and to use it to determine the presence of a defect of at least one defect type and / or the quality index which allows the assessment of the quality of the object. [6] Device according to claim 5, characterized by that the data processing device uses a maximum image of the image information to determine the presence of an error of at least one error type and / or the quality indicator, which was determined from the at least two pieces of image information acquired in a matrix during illumination from at least two directions of the area illumination device. [7] Device according to one of the preceding claims, characterized by that the line lighting device emits a line-shaped HDR reflection bright field illumination. [8] Device according to one of the preceding claims, characterized by that the matrix camera is calibrated in such a way that the data processing device can take into account perspective and optical distortion from the image information captured in the matrix. [9] Device according to claim 8, characterized by that the data processing device is set up in such a way that, after taking into account the perspective and the optical distortion, it determines at least one dimension of the object and / or at least one size of a determined error. [10] System with a first device for inspecting 3-dimensional objects according to one of the preceding claims and a second device for inspecting 3-dimensional objects according to one of the preceding claims, wherein the second device for inspecting 3-dimensional objects is arranged behind the first device for inspecting 3-dimensional objects in the transport direction of the object to be inspected, wherein the underside of the object is inspected by means of the second device for inspecting 3-dimensional objects, which underside is on top after the object has been turned over after the first device for inspecting 3-dimensional objects. [11] Method for inspecting 3-dimensional objects, in particular pouch cells, each object having a substantially pillow-shaped or cuboid-shaped housing with a top side and a bottom side, wherein the upper side of the housing is composed of at least one upper surface section and a plurality of lateral surface sections which are inclined, parallel or perpendicular to the at least one upper surface section or which represent corner sections, wherein the underside of the housing is composed of at least one lower surface section on the underside and a plurality of lateral surface sections which are inclined, parallel or perpendicular to the at least one lower surface section or which represent corner sections, the method comprising the following steps: • Capturing movement information with regard to a relative movement of each object to be inspected to a line illumination device (movement state) and with regard to an arrangement of the respective object in a predetermined position and over a predetermined period of time with regard to an area illumination device (rest state) by means of a movement detection device, • Illuminating a linear area of the top of the object to be inspected using the line illumination device, which, for example, emits a linear HDR reflection bright field illumination, • Illuminating the entire top of the housing of the object to be inspected from above in its resting state using the surface lighting device, • Acquisition of image information in a field of view by means of a matrix camera arranged above the rest position of the object to be inspected, wherein the field of view ◯ for line-by-line detection of the light of the line illumination device reflected from the line-shaped area of the upper side in the moving state of the object to be inspected and ◯ for the matrix-wise detection of the light of the surface illumination device reflected upwards from the entire upper side in the rest state of the object to be inspected, including the light reflected from the lateral surface sections of the object to be inspected, optionally via at least one first deflection mirror into the matrix camera, wherein the at least one first deflection mirror is arranged next to one side of the housing in the rest state of the object, • Receiving and processing the image information recorded by the matrix camera and the recorded movement information by means of a data processing device, wherein the data processing device assigns the image information recorded line by line in the moving state and the image information recorded matrix by matrix in the idle state to the respective object to be inspected and determines from this image information the presence of an error of at least one error type and / or determines a quality index which allows an assessment of the quality of the object. [12] Method according to claim 11, characterized by that the illuminated linear region extends over the entire length of the upper side and / or that at least four first deflecting mirrors are provided, each deflecting mirror being arranged next to a side of the housing when the object is at rest. [13] Method according to one of claims 11 to 12, characterized by that the matrix camera captures at least one of its images in a capture sequence • line by line the light of the line illumination device reflected from the line-shaped area of the top side in the moving state of a first object and • the light of the surface illumination device reflected upwards from the entire upper side in the rest state of a second object and the light reflected from the lateral surface sections of the second object into the matrix camera via the at least one first deflection mirror, wherein the second object is different from the first object, are recorded in a matrix manner. [14] Method according to one of claims 11 to 13, characterized by that the surface illumination device illuminates the entire top side of the housing of the object in the resting state of the object to be inspected in succession from at least two different directions obliquely from above and that the matrix camera captures the image information of the matrix camera for the illumination from at least two directions of the area illumination device in a matrix-wise manner and that by means of the data processing device the at least two items of image information recorded in a matrix are received and processed accordingly during illumination from the at least two directions of the area illumination device, this image information is assigned to the respective object and is used to determine the presence of an error of at least one error type and / or the quality index, wherein the data processing device preferably uses a maximum image of the image information to determine the presence of an error of at least one error type and / or the quality index, which was determined from the at least two items of image information recorded in a matrix during illumination from the at least two directions of the area illumination device. [15] Method according to one of claims 11 to 14, characterized bythat the matrix camera is calibrated in such a way that perspective and optical distortion from the image information acquired in matrix form can be taken into account by the data processing device, wherein at least one dimension of the object and / or at least one size of a determined error is determined preferably by means of the data processing device after taking the perspective and the optical distortion into account.

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