Predictive Quality Control / solino Application note ^)tO100-SO-25-MAN-001 Battery Components Opto solino RTI sensorDetecting micro defects using BRDF computational imaging Customer Task: Battery components such as button cells, coin batteries and cylindrical battery housings must meet strict quality and safety standards. Surface defects can affect sealing, electrical contact, corrosion resistance and overall product reliability. Manufacturers therefore require reliable inspection methods for: • coin cell batteries (e.g. CR series) • cylindrical battery housings • stamped and engraved markings • sealing surfaces and edges • coated and metallic surfaces The goal is to detect surface defects early in the production process and ensure consistent quality across all battery components before assembly or shipment. Inspection Challenges: Inspecting battery surfaces is challenging for conventional machine vision systems due to a combination of material properties, geometry and process variability. • Highly reflective metallic surfaces on housings (nickel, stainless steel, aluminum) create strong reflections and glare, which can hide defects or create false detections. • Curved edges of Coin cells and cylindrical batteries have rounded edges that cause changing light reflections. • Low-contrast engravings and laser markings, stamped text or logos have minimal contrast and are difficult to detect reliably • Micro-scale defects, scratches, dents, coating irregularities or early corrosion are often in the micron range and barely visible • Surface contamination and dust particles, oil residues or process contamination can affect sealing, electrical contact and long-term reliability. • Coating and material inconsistencies like thin coatings, oxidation layers or surface treatments may vary locally and are difficult to detect visually. • Process-related variability, differences in stamping, forming or coating processes lead to variations in surface appearance that complicate automated inspection. • Unstable inspection results of traditional vision systems depend heavily on fixed lighting setups, leading to inconsistent results when surface reflectance changes. Fig. 2: 100-SO-25-MAN-001 solino 25x25 - Viewer - Stand Opto GmbH • Floriansbogen 2/4 • 82061 Neuried • Germany • Tel: +49 89 8980 55 0 • info@opto-

Technology Principle Within milliseconds the system captures a series of 64 images under multiple illumination directions and reconstructs the surface reflectance information computationally. Key elements include: • coin cell surfaces and engraved labels • cylindrical battery housings • sealing edges and contact areas • surface coatings and finishes • contamination or handling damage • 64 multi-directional LED illumination • photometric stereo image capture • reflectance-based surface reconstruction • computational defect enhancement Because the system captures the surface under 64 illumination...