Rapid detection method for tooth surface waviness of involute helical gear based on wavelet transformation

Through wavelet transformation technology, high-density data sampling and multi-scale decomposition of the involute helical gear tooth surface is solved, and the problems of low efficiency and insufficient accuracy in tooth surface corrugation detection are achieved, and efficient and accurate gear quality detection and manufacturing optimization are achieved.

CN120429616APending Publication Date: 2025-08-05HEFEI JIUSHAO INTELLIGENT TECH CO LTD
5 Cites 2 Cited by

Patent Information

Application Number
CN202510587577.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-08
Publication Date
2025-08-05

AI Technical Summary

Technical Problem

The existing tooth surface corrugation detection methods are inefficient, insufficient anti-interference ability and limited extraction accuracy, making it difficult to meet the needs of high-quality gear production.

Method used

The tooth surface corrugation detection method based on wavelet transformation is adopted. Through high-density data sampling, multi-scale wavelet decomposition and threshold optimization algorithm, the tooth surface corrugation signal is separated and non-correlated signal interference is suppressed. Combined with rapid analysis process and automatic parameter adjustment, efficient and accurate corrugation extraction is achieved.

Benefits of technology

It significantly improves detection accuracy and stability, improves measurement efficiency, and can achieve real-time corrugation feature extraction in a mass production environment, providing reliable gear quality inspection and manufacturing optimization basis.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses an involute helical gear tooth surface waviness rapid detection method based on wavelet transformation, relates to the technical field of gear measurement and evaluation, and aims to solve the problems of low efficiency, insufficient anti-interference capability and limited extraction precision in existing tooth surface waviness detection. High-density data sampling is carried out on a specified tooth surface of an involute helical gear, a multi-scale characteristic decomposition signal of wavelet transform is utilized, a tooth surface waviness signal is separated from complex morphology characteristic components, and in combination with a threshold optimization algorithm, detail characteristics of waviness are effectively reserved, and meanwhile interference of non-correlation signals is restrained. In addition, efficient data processing and real-time waviness feature extraction are realized by designing a rapid analysis process and an automatic parameter adjustment mechanism. The method is suitable for gear quality detection in a batch production environment, and provides a reliable basis for defect diagnosis and optimization in a gear manufacturing process. Compared with a traditional detection method, the method has remarkable advantages in the aspects of detection precision, stability and speed.
Need to check novelty before this filing date? Find Prior Art