This Master’s thesis work is about the conditions that can affect the identification of defects in lithium-ion batteries. In lithium batteries, there are cathode and anode, which are separated with electrically insulative but ionically conductive electrolytes and separators. The cathode consists of aluminum foil, and the anode consists of copper foil. During the production of the batteries, these foils are being coated with active materials. Unfortunately, some defects such as scratches, misshape, marks, or spots can occur during the coating, and these defects harm the batteries’ performance. To ensure a good performance of the batteries, these defects have to be detected and identified. Today, these defects can be detected with a camera that scans the foils. To detect all the defects, even smaller marks, and dots, a new high-resolution sensor was investigated. The new sensor called Contact Image Sensor that acquires the raw information of the light and converts it into electrical signals was used. In other words, all the objects reflect the light, and the Contact Image Sensor captures the reflected light from the objects. By investigating the light, illumination time, and the contrast of the objects, it is possible to make the defects more visible. Furthermore, this thesis covers the speed of the coated foils passing the sensor. Since the production of the batteries should be time and cost-efficient, the speed can be high, but the sensor should have enough time to scan clear images. In this Master’s thesis work the implications of different sensor resolutions and contrast settings in a roll-to-roll process were studied, considering the speed of the process vs identifying defects (such as scratches and misshapes). It has also been studied if the light conditions, under normal circumstances, have a substantial impact on this trade-off. The result has been promising. Since the sensor has an internal resolution, a limited scanning area, and a scanning frequency, the speed will be limited by a maximum illumination time. But as long as the illumination time is under the maximum, the speed, in this case, does not matter. The maximum illumination time is different for different speeds. Furthermore, the results clearly show that high resolution, maximum allowed illumination time for each speed, and lower speeds, provide the best conditions for finding and identifying defects. However, there comes a price for the illuminated foil. The price is that the bright light can lead to overexposed foil, i.e., the part of the foil without coating will be exposed to too much light. This can create problems if damage to the foil is to be identified. Therefore, a more nuanced light that balances the contrast of the coating part and the foil is recommended. / Det här masterexamensarbetet handlar om förhållanden som påverkar identifieringen av defekter i litiumjonbatterier. Litiumbatterier består av katod och anod som separeras av elektriskt isolerande men joniskt ledande elektrolyt och separatorer. Katoden består av aluminiumfolie och anoden består av kopparfolie. Under tillverkningen av batterierna beläggs dessa folier med aktiva material. Tyvärr uppstår det ibland skador i beläggningen så som repor, missbildning, märke eller fläckar. Dessa defekter försämrar batteriets prestanda. För att säkerställa att batterierna fungerar felfritt måste dessa defekter upptäckas och elimineras. Idag används kamera som skannar folierna för att hitta defekter. För att upptäcka samtliga skador, bland annat mindre märken och fläckar, övervägs en ny högupplöst sensor. En ny sensor, så kallad Contact Image Sensor, som tar in ljuset och omvandlar det till elektriska signaler undersöks. Med andra ord fångar Contact Image Sensor det reflekterade ljuset från objekten. Genom att undersöka ljusets styrka, belysningstiden och kontrast är det möjligt att göra defekterna mer synliga. Vidare omfattar detta examensarbete hastigheten hos de folierna som passerar sensorn. Under en tids- och kostnadseffektiv produktion av batterier kan hastigheter av foljer vara höga, men sensorn måste ha tillräcklig tid och utrymme att skanna tydliga bilder. Påföljden av olika sensorupplösningar och kontrastinställningar i en roll-to-roll-process har därför studerats i detta examensarbete med hänsyn taget till processens hastighet kontra identifieringen av defekter (såsom repor och missbildningar). Det har också studerats om ljusförhållandena har, under normala omständigheter, en väsentlig inverkan på denna avvägning. Resultatet har varit lovande. Med anledning av sensorns interna upplösning, dess begränsade skanningsområdet, samt skanningsfrekvensen, kommer hastigheten att begränsas av en maximal belysningstid. Men så länge belysningstiden är under maximalt kommer hastigheten inte ha någon betydelse. Denna maximala belysningstid är olika för olika hastigheter. Vidare framgår det tydligt i resultatet att hög upplösning, maximal tillåtna belysningstiden för respektive hastighet, och lägre hastigheter, ger bästa förutsättningar för att hitta och identifiera defekter. Dock kommer ett pris för det upplysta beläggningen av foljen. Priset är att det ljusa ljuset kan leda till överexponerade folje, dvs där den delen av foljen som saknar beläggning utsätts för mycket ljus och kan bländas. Detta kan skapa problem om skador på foljen ska identifieras. En mer nyanserad ljus som balanserar kontrasten av beläggningen och foljen kan därför rekommenderas.

The diploma thesis deals with the research of CIS sensors, their parameters and market research. It contains a comparison between sensors and line cameras, which are used for similar purposes, therefore in this case for scanning the packaging material. The diploma thesis contains the described construction of the device, including the assembly of components and the design of boards. The work describes in detail the image processing from the CIS sensor and various image preprocessing. There is also described method of defect detection, including their distribution and individual approach to each type of defect. The thesis contains a description of the GUI, including its functions and connection to the application dealing with image processing.

Malfunctions on loom machines are the main causes of faulty fabric production. An on-loom fabric inspection system is a real-time monitoring device that enables immediate defect detection for human intervention. This dissertation presented a solution for the on-loom fabric defect inspection, including the new hardware design—the configurable contact image sensor (CIS) module—for on-loom fabric scanning and the defect detection algorithms. The main contributions of this work include (1) creating a configurable CIS module adaptable to a loom width, which brings CIS unique features, such as sub-millimeter resolution, compact size, short working distance and low cost, to the fabric defect inspection system, (2) designing a two-level hardware architecture that can be efficiently deployed in a weaving factory with hundreds of looms, (3) developing a two-level inspecting scheme, with which the initial defect screening is performed on the Raspberry Pi and the intensive defect verification is processed on the cloud server, (4) introducing the novel pairwise-potential activation layer to a convolutional neural network that leads to high accuracies of defect segmentation on fabrics with fine and imbalanced structures, (5) achieving a real-time defect detection that allows a possible defect to be examined multiple times, and (6) implementing a new color segmentation technique suitable for processing multi-color fabric defects. The novel CIS-based on-loom scanning system offered real-time and high-resolution fabric images, which was able to deliver the information of single thread on a fabric. The algorithm evaluation on the fabric defect datasets showed a non-miss-detection rate on defect-free fabrics. The average precision of defect existed images reached above 90% at the pixel level. The detected defect pixels' integrity—the recall scored around 70%. Possible defect regions overestimated on ground truth images and the morphologies of fine defects similar to regular fabric pattern were the two major reasons that caused the imperfection in defect pixel locating. The experiments showed the defect areas on multi-color fabrics could be precisely located under the proposed color segmentation algorithm.