• :

    Complex datasets of part measurements are increasingly common because of the wider availability of 3D range scanners and other advanced metrology in industry. Defect detection and process control based on such complex data structures are important but not fully explored. We present a new approach that uses the Laplace-Beltrami spectra to infer the intrinsic geometrical features of the surfaces of scanned manufactured parts. The isometric invariant property avoids the computationally expensive registration pre-processing of the data sets. The discriminatory power of several nonparametric multivariate statistics is tested on simulated data through permutation tests and distribution-free control charts. 

  • : Xueqi Zhao and Enrique del Castillo
  • : Pennsylvania State University
  • : Xueqi Zhao
  • : quality
  • : advanced/theoretical
  • : xuz206@psu.edu
  • : 607-379-5175
Laplace-Beltrami Spectra as a Tool for Statistical Shape Analysis of 2-D Manifold Data