3D scanning has become an essential tool for industrial inspection, yet no standardized framework exists to classify the complexity of scanned geometrical features. This study develops a quantitative complexity classification model to systematically evaluate inspection difficulty. Using a comparative SWOT analysis of major scanner types (structured-light, laser, and contact CMM), the study integrates product design parameters, geometric characteristics, and inspection constraints into a five-level framework: basic, simple, medium, complex, and high complexity. Each level is defined by measurable indicators such as feature count, surface topology, hole depth-to-diameter ratios, and free-form surface presence. Case studies using an Artec Leo scanner demonstrate that scanning effort, viewpoint planning, and data processing time increase predictably with complexity level. Quantitative results confirm this trend: Levels 1–2 required minimal scanning and exporting time (≈ 4.5 min), whereas Levels 4–5 required multiple rescans and extended post-processing. These findings demonstrate the predictive power of the framework for estimating inspection effort, guiding viewpoint planning, and optimizing cycle time. The proposed framework improves alignment between scanner capability and product requirements, enabling more efficient inspection planning, hybrid measurement strategies, and digital quality control workflows. This work provides a reproducible foundation for future research on automated complexity-aware inspection in Industry 4.0 environments.
Lin et al. (Thu,) studied this question.