Introduction The process of measuring the dimensions of a blind hole is difficult because the interior surfaces and base are hard to probe using normal probes and camera equipment. Optical measurement is additionally affected by restricted visibility, lighting differences, reflectance, view angle, and robotic positioning accuracy. Methods An adaptive robotic optical metrology system using AI is suggested to achieve non-contact hole inspection in this paper. The system consists of a six DOF robot, an optical measurement head with a camera, adaptive lighting system, AI segmentation, sub-pixel circle and ellipse detection, and robot-camera calibration with error correction using CMM reference data. The system was tested using 30 samples having holes with diameter ranges of 5-15 mm, depth range of 10-45 mm, aspect ratio of 2.0-3.1, three different surface reflectivity conditions, and angle range of 0-20°. Results All trials were successfully carried out for detecting the holes. Average position error in the three-dimensional space was 0.0322 mm; the RMSE of position error was 0.0344 mm; the maximum position error was 0.0645 mm. The average absolute diameter error was 0.0544 mm; RMSE of the absolute diameter error was 0.0647 mm. Average repeatability was 0.0217 mm; average confidence in AI-based segmentation was 0.935. Average position errors varied between 0.026 mm and 0.037 mm depending on reflectivity classes and increased at 15° and higher approach angles. Discussion The results offer initial proof-of-concept validation for the use of AI-aided adaptive optical robotic systems to perform automated and contactless inspections of blind holes at least within the current scope of operation. Nevertheless, the small number of samples used, single replication and partially confounding experimental conditions pose a limit on the generalizability of the results. Additional research is necessary using multiple trials, ablation tests of modules, and larger depth-to-diameter ratios are required to establish the system’s robustness and measurement reliability.
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Giriraj Maannayee (2026) studied this question.
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