Industrial statistics and manifold data

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Complex and not only big data exist everywhere in industry and how to control and optimize systems based on these data types is an important aspect of modern Quality Engineering. One fundamental type of complexity occurs when data lies on a lower dimensional, curved subspace or manifold. We review a new approach for statistical process monitoring of point cloud, mesh and voxel data based on intrinsic geometrical features of the 2-D manifold (surfaces) of scanned manufactured parts. Monitoring intrinsic properties avoids computationally expensive registration pre-processing of the data sets. We also present a review of recent approaches for analyzing and designing experiments where either the response or the covariates lie on manifolds.

Original languageEnglish (US)
Pages (from-to)155-167
Number of pages13
JournalQuality Engineering
Volume32
Issue number2
DOIs
StatePublished - Apr 2 2020

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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