  Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM)
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 paper, we utilize 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.
