Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization
This work addresses process modeling and optimization for manufacturing using point cloud data, but it appears incremental as it applies existing multilinear algebra techniques to a specific domain.
The paper tackles the challenge of modeling high-dimensional and complex point cloud data from 3D metrology by proposing tensor regression approaches to link variational patterns to process variables, with performance evaluated through simulations and a real turning process optimization case study.
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.