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

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 ana...

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Bibliographic Details
Published in:Technometrics 2019-07, Vol.61 (3), p.385-395
Main Authors: Yan, Hao, Paynabar, Kamran, Pacella, Massimo
Format: Article
Language:English
Subjects:
3-D technology
Cloud computing
Computer simulation
Coordinate measuring machines
Data analysis
Modelling
Optimization
Process modeling
Process variables
Regression analysis
Regression models
Regularized tensor decomposition
Regularized tensor regression
Scanners
Structured point cloud data
Tensors
Three dimensional models
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Summary: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.
ISSN:0040-1706
1537-2723
DOI:10.1080/00401706.2018.1529628