Strategy Adaptive Particle Swarm Optimization Algorithm for Solving Free-Form Surface Registration to Improve Detection Accuracy

The registration between the design model and the actual measurement model is a key problem in the detection of free-form surface parts. To improve the accuracy and robustness of free-form surface parts inspection, a strategy adaptive particle swarm optimization (SAPSO) algorithm is proposed. Using...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-14
Main Authors: Li, Yaru, Zhang, Ruijie, Luo, Zai, Tang, Yingqi, Jiang, Wensong, Cheng, Yinbao, Wang, Qiyue
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Algorithms
Atmospheric measurements
Design optimization
Euclidean geometry
Extraterrestrial measurements
Free form
Free-form surface
fringe projection profilometry (FPP)
metaheuristic algorithm
Optical variables measurement
Optimization
Parameters
Particle measurements
Particle swarm optimization
particle swarm optimization (PSO)
Phase measurement
point cloud registration
Robustness (mathematics)
Surface morphology
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The registration between the design model and the actual measurement model is a key problem in the detection of free-form surface parts. To improve the accuracy and robustness of free-form surface parts inspection, a strategy adaptive particle swarm optimization (SAPSO) algorithm is proposed. Using the parameters composed of rotation and displacement variables as individual parameters, taking the minimum Euclidean distance between two models as the objective function and evolving from generation to generation, the calculation results can be guaranteed to be the global optimal solution. In SAPSO, an adaptive mechanism of inertia weight and velocity update strategy of particles is proposed to balance the global search and local exploration capabilities. The SAPSO algorithm is compared with other particle swarm optimization (PSO) variants. Simulation and actual measurement experiments show that the proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the effectiveness and practicability of SAPSO algorithm are verified.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3453320