Photometric stereo-based high-speed inline battery electrode inspection

3.1 Acquisition and control

For a deeper understanding of the inline image acquisition process, the generation of the trigger signals for the light sources and the camera are described in detail. For sake of clarity, our “high-accuracy” hardware configuration is described first, and the changes necessary to achieve higher scanning speeds are described later in the text.

According to the blockdiagram in Fig. 3, an FPGA-based controller (“Trigger Hardware”), developed in our group, synchronizes control of lights and camera acquisition with material motion. Firstly, the trigger hardware decimates the pulses from an optical encoder with 5 µm increments to frame trigger pulses with 50 µm increments, thus defining the resolution in transport direction as 50 µm per pixel. Secondly, for each frame trigger pulse, i. e. each 50 µm of transport movement, the controller switches on a different light of the four lights, while the other lights are off, and triggers an image acquisition by the camera.

Figure 4 shows the timing for switching the lights and triggering the camera for the high-accuracy hardware configuration at maximum speed, with a frame trigger rate of 10 kHz, corresponding to a material speed of 0.5 m/s. The frame period is the inverse of the frame trigger rate and equals to 100 µs, corresponding to a material progress of 50 µm. The exposure time of the camera is set to 80 µs.

To be able to acquire each object point under four different illumination conditions, four rows of the camera’s pixel matrix are read out per frame trigger. The spatio-temporal sampling of the moving object is illustrated in Fig. 5, showing that each object point (A, B, C, …) is acquired four times.

As illumination, four AIT xposure:flash^[1] [7] line light sources are located in a 4-orthogonal-configuration around the camera’s field of view. Each light source contains a linear array of white high-power LED’s that allow fast strobing, and a cylinder Fresnel lens for collimating the radiated light.

The detailed, geometric relations between the directions of the four illuminations, the camera view, the object transport and the orientation of the object’s surface, with respect to a Cartesian coordinate system, are schematically shown in Fig. 6. Viewing from the top, the light sources are mounted at ϕ = 45 ° rotation with respect to the transport direction, so to deliver high quality control data for defects that often occur in transport direction. They are mounted with a polar angle of approx. θ = 54.7 °. This four-light constellation is optimal from algebraic considerations [8] and has been experimentally validated for the electrode material by acquiring an electrode using a 32 light source dome fixture and selecting the four-light tuple that provided the best results in terms of mean angular error of reconstructed surface normals compared to the full 32 light surface reconstruction. The viewing direction of the camera is perpendicular to the plane of object’s surface (battery foil). Per frame, the camera acquires only a small fraction of the object’s surface indicated by which we refer to as the field of view (FOV). The line lights 1 to 4 are mounted at the indicated four points and are oriented to illuminate the FOV area. The distance between light emitting area and FOV is 239 mm. The fan of relevant light rays emerging from line light 3 is symbolized by the dashed lines from line light 3 to the two ends of the FOV. From this consideration it becomes clear that due to the slanted mounting of line light 3 over the FOV, one end of the FOV is nearer to the line light 3 than the other one. This leads to a gradient of the illuminance along the FOV. The same holds for the other light sources in a similar manner.

Note that regarding the light sources as concentrated spots is a simplification. In reality, the light emitting area of the line lights is a longish rectangle (approx. 150 x 15 mm). Nevertheless, within the limits of accuracy of the photometric stereo algorithms presented here, the effect of this idealization can be neglected.

The directions of the light sources are described by the direction vectors (length scaled with arbitrary factor) from the center of the FOV to the centers of the light-emitting areas of the four line light sources. They are defined by l 1 = ( 1 , 1 , 1 ) T , l 2 = ( − 1 , 1 , 1 ) T , l 3 = ( − 1 , − 1 , 1 ) T , l 4 = ( 1 , − 1 , 1 ) T , respectively.

We use four light sources for their increased surface reconstruction stability compared to the three lights that are the minimum requirement for determining three dimensional surface normal vectors [8]. In order to have enough light for a proper signal, the light sources are strobed at a frequency of 10 kHz, which is only a small fraction of their maximum strobing frequency of 600 kHz. The illuminance in the object plane, generated by a single line light, is approximately 500.000 lx.

The camera, a Mikrotron EoSens 4CXP area-scan camera with 2336(H) × 1728(V) pixels, 7 µm pitch, monochrome version, is equipped with a Schneider-Kreuznach Xenoplan 2.0/28 lens with 29.29 mm focal length, mounted with 226 mm working distance (WD) for a magnification of 0.14 to realize an optical resolution of 50 µ m / px, which is equal to the resolution in transport direction. Using this configuration we achieve a FOV of approximately 116 mm perpendicular to transport direction, and 200 µm in transport direction. The FOV corresponds to a region of interest of 2336 × 4 pixels configured in the camera. For sake of clarity, FOV, WD, and the transport direction are shown in Fig. 6.

The FOV together with the resolution of 50 µ m / px were design requirements to be able to acquire the whole width of the material as well as the transition from substrate to the material at a resolution suitable for the expected defects’ size.

We have constructed and successfully tested a system prototype in our laboratory. Figure 7 shows a photograph of the prototype system, including a motorized roll simulation that allows us to thoroughly test system performance under various operating speeds.

Figure 4

Timing of illumination and camera triggering. Material motion, as registered by an encoder, causes a camera exposure, while light sources from each direction are activated in turn. In high-accuracy configuration, the frame period is 100 µs. In high-speed configuration, the frame period is 25 µs.

Figure 5

Multi-line scanning with four light sources. The camera acquires a region of interest of 4 rows, corresponding to the number of light sources, during material motion.

Figure 6

Schematic of geometric relations between the directions of the four illuminations, the camera view, the object transport and the orientation of the object’s surface.

Figure 7

Illustration of our system prototype attached to a roll simulator operating in our laboratory. On top, the camera can be seen, while in middle area, line light sources are visible focusing on the roll in the lower region, which is driven by a motor on the right.

Up to here, the high-accuracy configuration of the acquisition and control system was described. For faster scanning, we have developed a high-speed system. The differences between the two system variants shall be described now.

In order to speed up the acquisition, a faster camera, the AIT xposure:camera, monochrome version, is used and the number of xposure:flash light sources is reduced from four to two. The timing of Figure 4 reduces to toggling of two lights at a frame period of 25 µs. The direction vectors of Fig. 6, are then defined as l 1 = ( 1 , 0 , 1 ) T and l 2 = ( − 1 , 0 , 1 ) T .

The xposure:camera is based on a high-speed sensor with 2016(H) x 60(V) light-sensitive pixels, 9 µm pitch, allowing single-line readout of up to 600.000 frames per second (fps). Corresponding to the two light directions, two of the 60 lines of the pixel matrix are read out. A Schneider-Kreuznach Apo-Componon 2.8/40 lens with 41.52 mm focal length, mounted at a WD of 255 mm, is used to achieve a magnification of 0.18 for realizing again a resolution of 50 µ m / px. Due to light limitation, in order to have a good signal, the test images described later are acquired at 40 kHz frame trigger rate, with exposure time of 24 µs, corresponding to a scanning speed of 2 m/s.

3.2 Data processing

An industrial-grade computer with Microsoft Windows 10 operating system is used for configuration of the acquisition subsystem and processing of acquired image data. Image data from the Mikrotron camera is acquired via an Active Silicon FireBird CoaXPress Frame Grabber (Quad CXP-6). Image data from the xposure:camera is acquired via an Intel Ethernet-Converged-Network-Adapter X520-SR2 (2x10 Gigabit Ethernet). Photometric stereo results are processed, and stored to hard disk for further analysis. A graphical user interface is provided in order to enable an operator to review the results in real-time. As the PC is located within the machine, its user interface is remotely accessible via Ethernet-based networking.

3.3 System calibration

In order to achieve optimal performance in terms of image quality, we perform a flat field correction (FFC) calibration step prior to scanning.

FFC serves two purposes. The first is to mitigate dark signal non-uniformity and photo response non-uniformity of the camera sensor. Secondly, it compensates the previously described gradient of illuminance along the FOV, caused by the slant of the respective light source relative to the object’s surface. By application of the FFC to the acquired raw image data, we approximate an assumption needed for the photometric stereo (PS) algorithm [9], namely that light sources appear infinitely far away from the observed surface. Therefore it is obvious that we have to perform FFC individually for each of the available light sources, i. e. manage four sets of FFC coefficients.

To calculate the FFC coefficients (offset and gain parameters per pixel and per light source), we acquire 1000 camera frames with all lights turned off and 1000 camera frames per light source (all but one light source turned off). The average over all dark acquisitions is denoted as black frame B ∈ R V × H . In a second step, we turn on a single light k, while all others are turned off, and acquire 1000 camera frames per light source, with a white, flat, quasi-Lamertian object in its field of view, average the data pixelwise and denote the results as white frames W k ∈ R V × H for light source k with 1 ≤ k ≤ n.

Using dark- and white-frames, we derive gain values G k = 2 m W k − B for each light source k. Here, matrix operations to be done element-wise, and m is number of bits of the output image, in our case m = 16 bit. The process up to here is the FFC calibration, which has to be done in regular intervals of days or weeks or if relevant things have changed. In regular operation, the calibration parameters are applied to the acquired raw frames. From raw frames R k with light k turned on, corrected frames, C k , are calculated, using C k = ( R k − B ) G k , multiplications to be done element-wise.

5.1 High-accuracy, four light setup

Our dataset comprises two black colored nickel manganese cobalt (NMC) cathodes and two graphite anodes of dark gray texture. Coating was applied with a width of 100 mm onto substrates of approximately 130 mm width. The substrate’s thickness is 20 µm, whereas, the applied coating thickness ranges from 30 to 450 µm. The samples were acquired at a speed of 500 mm/s. The results were obtained using the processing pipeline described in Section 4.

The most obvious type of defect is missing coating, as illustrated in Fig. 8 (a,b). In the present samples it is caused by agglomerates clogging the doctor blade and preventing new slurry to pass the blade in those regions. Sometimes these pollutions break free after a while, as can be seen in Fig. 8 (a). Electrodes with missing coating are unfit for use in cells. Large scale missing coating is obviously visible in raw image data, small blade obliterations, however, may not reach down to the substrate material.

Such defects can be called coating inhomogeneities. An example can be seen in Fig. 8 (c). Coating inhomogeneities can cause, among others, degraded cell capacity [3], and can be mitigated to some extent by subsequent calandering. Inhomogeneities are hard to detect optically, especially on the black NMC material. They are, however, visible in surface normals and the derived depth map.

Pinholes are small diameter pores, depicted in Fig. 8 (c), are caused by small air bubbles bursting in the drying process and can reach down to the substrate. Pinholes can be caused by inadequate slurry mix, or drying parameters [3]. They are best visible in depth maps and invisible in raw image data. Note that the shown sample additionally exhibits small dark spots that can, but don’t always coincide with pinholes. Slurry containing large air bubbles can leave cavities or void areas, as shown in Fig. 8 (d). This sample further contains cracks in the coating area.

While all the previously discussed defects are variations of missing active material, agglomerates constitute of excess material, as shown in Fig. 8 (d). Agglomerates can be caused by an incomplete slurry mix [3] and can potentially damage the calandering roll, or if not crushed there, pierce the separator foil in final cells.

Another observable coating property is the coating’s surface roughness. Rough coating can damage the mechanical press in the calandering process following the coating step. Examples for low roughness is shown in Fig. 8 (a,b), while a sample of high roughness can be seen in Fig. 8 (c).

Figure 8

Coating defects occurring in two black NMC coated cathodes (a,b) and two dark gray graphite anodes (c,d). Defects are marked in red. Missing coating is present in (a,b). Phinholes are visible on the left hand side of (c), whereas the right hand side of (c) shows a coating inhomogenity. Agglomerates, cavities and cracks are present in (d).

5.2 High-speed, two light setup

In this section we present a qualitative comparison to results of an implementation of the proposed inspection system architecture that is designed towards even higher operational speeds. Here, we choose an acquisition speed of 2000 m m / s. The setup, illustrated in Fig. 9, maintains the same acquisition control mechanism as presented in Section 3 with two notable differences. First, here we employ a high-speed multi-line-scan camera, an AIT xposure:camera [12]. The camera’s sensor delivers 60 lines of 2048 pixels width and can be clocked at frequencies of up to 600 kHz in grayscale operation. The optics are chosen for a 100 m m field of view at a resolution of 50 µ m per pixel. At a speed of 2000 m m / s the system trigger rate is 40 kHz with a camera exposure of slightly below 25 µ s. Secondly, this setup uses only two line light sources which are mounted vertically in line with the camera axis and tilted ± 45 ° relative to the transport direction. Correspondingly, two of the 60 available sensor lines are used to acquire data. Using two light sources, prevents full, two dimensional, surface reconstruction, as that would require at least three light sources. Hence, we use the standard PS algorithm for processing. We acquire the same electrode sample as presented in Fig. 8 (c), perform one dimensional PS according to Eq. (2) and albedo, and present resulting normals in terms of a gradient image that is generated by applying Eq. (7).

As shown in Fig. 10, surface features – such as the vertical coating inhomogenity marked on the right hand side of each plot – are still clearly distinguishable despite the increased speed and lower number of lights used for surface reconstruction. Turning the attention to the crack in the electrode surface on the left side of the plot, we can see the vertical section of the crack vanishing on the shown gradient in y-direction. The gradient in x-direction is not reconstructed due to using only two lights.

Figure 9

Our high-speed xposure multi-line-scan camera setup mounted on a roll simulator. Two line light sources are mounted in parallel to the visible slit and tilted ± 45 ° relative to the transport direction.

Figure 10

Comparison of reconstruction results of the two used setups. Left: Multi-line-scan camera with 2 light sources acquired at 2000 m m / s. Right: Area-scan camera and 4 light sources acquired at 500 m m / s. While surface features start to fade into noise in the high-speed case, most of them are still visible, as can be seen with the crack on the left-hand side of the sample.