Hybrid Photometric-Geometric 3D Reconstruction Framework for Manufacturing-Oriented Assessment of Tablet Surface CQAs under PAT/RTRT.

Surface-related critical quality attributes (CQAs) of solid oral dosage forms influence mechanical integrity, coating uniformity, imprint readability, and downstream product performance. Conventional two-dimensional visual inspection and geometry-focused three-dimensional (3D) systems primarily assess gross deviations but often lack mechanistic surface descriptors relevant to CQA interpretation. This study tested the hypothesis that integrating complementary photometric and geometric imaging modalities can enhance sensitivity to surface-related CQAs beyond single-modality inspection. A hybrid photometric-geometric reconstruction framework was developed for manufacturing-oriented surface integrity assessment. A hybrid photometric-geometric 3D reconstruction pipeline integrating photometric stereo and structured-light acquisition captured complementary local reflectance and global geometric information. Depth maps derived from both modalities were spatially aligned and fused to generate a unified surface representation. Surface descriptors, including curvature entropy and roughness metrics, were evaluated to assess surface complexity and stability. Micro-defect detectability was examined to compare reconstruction modalities under controlled experimental conditions. The hybrid reconstruction approach demonstrated improved balance between local imprint fidelity and global surface continuity compared with single-modality methods, while maintaining reproducible surface metrics across repeated sessions. Surface descriptors provided structured measures of surface variation beyond visual inspection alone. The proposed framework is positioned as a CQA-linked surface anomaly assessment tool within a Process Analytical Technology (PAT) context, rather than as a direct surrogate for traditional release testing. By enhancing mechanistic interpretation of surface variability, the approach may support risk-based PAT strategies in manufacturing. This proof-of-concept study used a small dataset (n = 4), requiring further validation with larger, diverse samples.