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 is a challenge. In this paper, we utilize techniques developed in multilinear algebra and propose a set of tensor regression approaches to model the variational patterns of point clouds and link them to process variables. The performance of the proposed methods is evaluated through simulation and a real case study of turning process optimization.
Point Cloud Data Analysis for Process Modeling and Optimization
PACELLA, Massimo;
2017-01-01
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 is a challenge. In this paper, we utilize techniques developed in multilinear algebra and propose a set of tensor regression approaches to model the variational patterns of point clouds and link them to process variables. The performance of the proposed methods is evaluated through simulation and a real case study of turning process optimization.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
