Improved sphericity error evaluation combining a heuristic search algorithm with the feature points model

Huang, Jingzhi; Jiang, Lin; Chao, Xiangzhang; Ding, Xiangshuai; Tan, Jiubin

doi:10.1063/1.5086251

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…The capability of the micro-scale spherical and cylindrical surface modeling is established based on the model fitting accuracy [55,56]. Therefore, the contribution of the proposed technique is determined based on the model fitting to the surface.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Rodríguez

2022

Algorithms

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“…Different processes have varying requirements for sphericity error. Accurately evaluating the sphericity error in thin-walled spherical shells is crucial for achieving the high-precision manufacturing of HRGs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…In order to address the challenge of sphericity error evaluation based on the minimum zone criterion, Chen et al [ 29 ] established four concentric sphere models by solving systems of linear algebraic equations and selected the one with the minimum sphericity error as the final evaluation result. This method laid the foundation for approaches based on selecting sample points [ 12 ]. Fan et al [ 30 ] proposed a minimum potential energy method inspired by this method.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Population Approach for Sphericity Error Evaluation in the Manufacture of Hemispherical Shell Resonators

Zhao,

Cui,

Bian

et al. 2024

Sensors

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The performance of a hemispherical resonant gyroscope (HRG) is directly affected by the sphericity error of the thin-walled spherical shell of the hemispherical shell resonator (HSR). In the production process of the HSRs, high-speed, high-accuracy, and high-robustness requirements are necessary for evaluating sphericity errors. We designed a sphericity error evaluation method based on the minimum zone criterion with an adaptive number of subpopulations. The method utilizes the global optimal solution and the subpopulations’ optimal solution to guide the search, initializes the subpopulations through clustering, and dynamically eliminates inferior subpopulations. Simulation experiments demonstrate that the algorithm exhibits excellent evaluation accuracy when processing simulation datasets with different sphericity errors, radii, and numbers of sampling points. The uncertainty of the results reached the order of 10−9 mm. When processing up to 6000 simulation datasets, the algorithm’s solution deviation from the ideal sphericity error remained around −3 × 10−9 mm. And the sphericity error evaluation was completed within 1 s on average. Additionally, comparison experiments further confirmed the evaluation accuracy of the algorithm. In the HSR sample measurement experiments, our algorithm improved the sphericity error assessment accuracy of the HSR’s inner and outer contour sampling datasets by 17% and 4%, compared with the results given by the coordinate measuring machine. The experiment results demonstrated that the algorithm meets the requirements of sphericity error assessment in the manufacturing process of the HSRs and has the potential to be widely used in the future.

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A Comprehensive Review on Computational Techniques for Form Error Evaluation

Pathak

Singh

2021

Arch Computat Methods Eng

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Improved sphericity error evaluation combining a heuristic search algorithm with the feature points model

Cited by 5 publications

References 25 publications

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

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

An Adaptive Multi-Population Approach for Sphericity Error Evaluation in the Manufacture of Hemispherical Shell Resonators

A Comprehensive Review on Computational Techniques for Form Error Evaluation

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