Evaluation of a photogrammetry-based scanner for measuring small-sized features in an additive manufacturing repair process chain

Abstract Repairing damaged or worn components, particularly those with high added value, has become a beneficial and sustainable practice, yielding significant economic and environmental impacts. These benefits arise from reductions in material usage, labor, and waste. A typical repair process chain combines additive manufacturing (AM), subtractive machining (SM), and measuring techniques to ensure that the involved technologies exchange accurate information regarding the dimensions and position of defects. Among the available measuring techniques, photogrammetry stands out for its low cost, flexibility, and proven ability to measure both simple and complex geometries. This study evaluated the effectiveness of close-range photogrammetry as an inspection tool within a repair process chain. The assessment focused on its capability to detect small features and measure slots with sub-millimeter dimensions. An experimental plan was developed to analyze the impact of slot dimensions and scanning strategy, taking into account limitations related to the depth of field. The best performance for the proposed outputs was observed with the widest and deepest features (slots), at a limited distance from the focus center, and with an optical axis tilt of 45°. This finding highlights the importance of feature sizes and optimal scanning strategies for real-world applications.