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

Area of Science:

• Metrology and Measurement Science
• Computational Geometry
• Mechanical Engineering

Background:

• Accurate sphericity error evaluation is crucial for quality control in precision manufacturing.
• Existing methods can be computationally intensive, limiting real-time applications.
• The need for efficient and accurate algorithms for geometric error assessment is growing.

Purpose of the Study:

• To develop a high-speed method for evaluating sphericity errors.
• To introduce a novel approach combining heuristic search with a feature points model.
• To enhance the efficiency and accuracy of sphericity error measurements.

Main Methods:

• A heuristic search algorithm combined with a feature points model (HSA-FPM) was developed.
• Initial sphere center and sphericity were calculated for candidate points.
• An iterative search determined the optimal sphere center using a heuristic algorithm and evaluation criterion.
• Feature points were identified to construct a sphericity evaluation model, with the Nelder-Mead algorithm used as an example.

Main Results:

• The HSA-FPM method achieved exact sphericity solutions with few iterations.
• The entire evaluation procedure took approximately 0.01 seconds.
• Evaluation efficiency improved by 26%-61% compared to previous methods.
• The method demonstrated complete agreement with established evaluation criteria.

Conclusions:

• The proposed HSA-FPM method offers a highly efficient and accurate solution for sphericity error evaluation.
• Its speed and precision make it suitable for real-time measurement and quality control applications.
• This advancement contributes to improved metrology in precision engineering.