Speaker
Description
Recent advances in additive manufacturing enable the fabrication of complex parts with intricate geometries and spatially-varying material composition. Data fusion integrates point cloud data with chromatic attributes, yielding 4D point clouds, a rich representation that jointly encodes shape and material information. We introduce a registration-free framework for jointly monitoring shape and surface color via 4D point clouds. The proposed approach leverages the Laplace-Beltrami operator to capture intrinsic spectral features. A combined monitoring scheme is developed to detect shape deformations and color anomalies, complemented by a spatially-aware post-signal diagnostic procedure to determine the source of change and localize color anomalies. Crucially, neither component requires point cloud registration or mesh reconstruction, thereby eliminating error-prone and computationally expensive pre-processing steps. The performance of the proposed framework is assessed through a Monte Carlo simulation study and a case study.
| Special/ Invited session | Young Statisticians |
|---|---|
| Classification | Both methodology and application |
| Keywords | Statistical Process Monitoring; Post-signal diagnostic; Laplace-Beltrami operator |