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

TL;DR

This paper introduces tensor regression methods to analyze high-dimensional point cloud data from 3D metrology tools, enabling better process modeling and optimization.

Contribution

It proposes novel tensor regression techniques specifically designed for structured point cloud data, addressing their high dimensionality and complexity.

Findings

Effective modeling of point cloud variations

Successful application to process optimization

Improved process control insights

Abstract

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.