An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

Tian, Xiaoqiang; Kong, Lingfu; Kong, Deming; Li, Yuan; Kong, Dehan

doi:10.1109/access.2020.3029563

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…In the same way, flat and free-form surface modeling has been developed through the algorithms of artificial intelligence [52,53]. These algorithms perform the parameter optimization by employing the traditional search structure [54,55], where the solution space is not defined through the data related to the surface model. Instead, the multi-objective optimization determines the solution space by employing contour data related to the surface model.…”

Section: Discussionmentioning

confidence: 99%

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Rodríguez

2022

Energies

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Industry 4.0 represents high-level methodologies to make intelligent, autonomous, and self-adaptable manufacturing systems. Additionally, the surface modeling technology has become a great tool in industry 4.0 for representing the surface point cloud. Thus, the micro-scale machining technology requires efficient models to represent micro-scale flat and free-form surfaces. Therefore, it is fundamental to perform surface modeling through artificial intelligence for representing small surfaces. This study addressed multi-objective optimization via genetic algorithms and micro laser line projection to accomplish surface models for representing micro-scale flat and free-form surfaces, where an optical microscope system retrieves micro-scale topography via micro laser line coordinates and the multi-objective optimization constructs the flat and free-form surface models through genetic algorithms and micro-scale topography. The multi-objective optimization determines the surface model parameters through exploration and exploitation, and the solution space is deduced via surface data. The surface model generated through the multi-objective optimization fit accurately to the micro-scale target surface. Thus, the proposed technique enhanced the fitting of micro-scale flat and free-form surface models, which were deduced via gray-level images of an optical microscope. This enhancement was validated by a discussion between the multi-objective optimization via genetic algorithms and the micro-scale surface modeling via optical microscope imaging systems.

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Section: Discussionmentioning

confidence: 99%

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Rodríguez

2022

Energies

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“…J. Apolinar Mu Muñoz Rodr Rodríguez et al and Xiaoqiang Tian et al used a genetic algorithm and BP neural network-based on particle swarm optimization to optimize the control point and knot sequence of the NURBS surface, respectively. There is improved accuracy and speed of fitting NURBS surfaces [26,27]. Jinho Song et al divide unorganized points into boundary points and internal points using a deep neural network, in order to facilitate more explicit parameterization of boundary points during NURBS modeling [28].…”

Section: Literature Reviewmentioning

confidence: 99%

A Reverse Modeling Method Based on CAD Model Prior and Surface Modeling

Fan

Wang

et al. 2022

Machines

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Reverse engineering is an important process of real model digitization. However, the existing methods are limited by the characteristics of the modeling object; the modeling is low efficient, has poor versatility, and unstable accuracy. To complete the reverse engineering of a real workpiece with irregular deformation based on the ideal computer-aided design (CAD) model, a high-precision reverse engineering method of the workpiece based on the CAD model prior was presented. Through the registration of the ideal CAD model and the point cloud model of the real workpiece, the geometric feature position information and feature constraint information contained in the CAD model are transmitted to the modeling process, which helps to improve the accuracy and efficiency of reverse engineering. This method is applied to the reverse engineering process of the engine compartment with slight irregular deformation, the modeling accuracy reaches 0.04 mm, and the high-precision reverse engineering of the engine compartment is successfully realized.

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“…The metaheuristic algorithms include algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing [31]. Metaheuristic algorithms such as particle swarm optimization, ant colony optimization, and simulated annealing have been implemented to construct mathematical models to represent the free-form surface [32,33]. For instance, the particle swarm makes the optimization via the position and velocity of particles [34].…”

Section: Introductionmentioning

confidence: 99%

Micro-Scale Spherical and Cylindrical Surface Modeling via Metaheuristic Algorithms and Micro Laser Line Projection

Rodríguez

2022

Algorithms

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With the increasing micro-scale manufacturing industry, the micro-scale spherical and cylindrical surface modeling has become an important factor in the manufacturing process. Thus, the micro-scale manufacturing processes require efficient micro-scale spherical and cylindrical models to achieve accurate assembly. Therefore, it is necessary to implement models to represent micro-scale spherical and cylindrical surfaces. This study addresses metaheuristic algorithms based on micro laser line projection to perform micro-scale spherical and cylindrical surface modeling. In this technique, the micro-scale surface is recovered by an optical microscope system, which computes the surface coordinates via micro laser line projection. From the surface coordinates, a genetic algorithm determines the parameters of the mathematical models to represent the spherical and cylindrical surfaces. The genetic algorithm performs exploration and exploitation in the search space to optimize the models’ mathematical parameters. The search space is constructed via surface data to provide the optimal parameters, which determine the spherical and cylindrical surface models. The proposed technique improves the fitting accuracy of the micro-scale spherical and cylindrical surface modeling performed via optical microscope systems. This contribution is elucidated by a discussion about the model fitting between the genetic algorithms based on micro laser line projection and the optical microscope systems.

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An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

Cited by 6 publications

References 24 publications

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

A Reverse Modeling Method Based on CAD Model Prior and Surface Modeling

Micro-Scale Spherical and Cylindrical Surface Modeling via Metaheuristic Algorithms and Micro Laser Line Projection

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