Structured Point Cloud Data Analysis Via Regularized Tensor Regression for Process Modeling and Optimization

Hao Yan, Kamran Paynabar, Massimo Pacella

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Advanced 3D metrology technologies such as coordinate measuring machine and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization. However, due to their high dimensionality and structure complexity, modeling and analysis of point clouds are still a challenge. In this article, we use multilinear algebra techniques and propose a set of tensor regression approaches to model the variational patterns of point clouds and to link them to process variables. The performance of the proposed methods is evaluated through simulations and a real case study of turning process optimization.

Original languageEnglish (US)
Pages (from-to)385-395
Number of pages11
JournalTechnometrics
Volume61
Issue number3
DOIs
StatePublished - Jul 3 2019
Externally publishedYes

Fingerprint

Point Cloud
Process Modeling
Process Optimization
Tensors
Data analysis
Tensor
Regression
Coordinate measuring machines
Process monitoring
Multilinear Algebra
Algebra
Coordinate Measuring Machine
Process Monitoring
Scanner
Metrology
Dimensionality
Lasers
Laser
Optimization
Modeling

Keywords

  • Process modeling
  • Regularized tensor decomposition
  • Regularized tensor regression
  • Structured point cloud data

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

Cite this

Structured Point Cloud Data Analysis Via Regularized Tensor Regression for Process Modeling and Optimization. / Yan, Hao; Paynabar, Kamran; Pacella, Massimo.

In: Technometrics, Vol. 61, No. 3, 03.07.2019, p. 385-395.

Research output: Contribution to journalArticle

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