Gear Tooth Profile Edge Extraction via Engagement-Pixel Tracking
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Solution Overview
Problem
Existing methods for designing and simulating complex gear tooth profiles are inefficient due to complex engagement equations and numerical instability, leading to inaccuracies in profile extraction, especially in complex curved surfaces like beveloid and non-circular gears.
Innovation Solution
The engagement-pixel image edge tracking method involves defining a transmission ratio, setting step sizes, acquiring and binarizing instantaneous contact images, and performing edge tracking with secondary extraction and compensation to improve accuracy, using principles like step-shaped tooth profiles and pixel absence correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional complex engagement equation of tooth surface is used, then theoretical accuracy can be maintained, but calculation efficiency becomes low and numerical instability occurs
Solution Approach 1:
The patent replaces the traditional mechanical/mathematical engagement equation system with an image processing system. By simulating the machining process and capturing engagement states as images, the complex numerical calculations are substituted with pixel-based edge tracking and image analysis, achieving both high accuracy and computational efficiency.
Solution Approach 2:
The patent creates a visual copy of the engagement process through image simulation. Instead of directly solving complex engagement equations, the system generates images representing the engagement states and extracts tooth profile information from these images, providing an alternative representation that avoids numerical instability while maintaining accuracy.
2Manufacturing precision
If three-dimensional Boolean operation method is used, then manufacturing precision is improved, but calculation efficiency becomes low
Solution Approach 1:
The patent substitutes complex three-dimensional Boolean operations with two-dimensional image processing operations. By representing the engagement state as images and using pixel-based edge tracking, the system achieves comparable precision without the computational burden of 3D Boolean calculations.
Solution Approach 2:
The patent extracts only the essential edge information from the engagement images using edge detection algorithms, rather than performing complete three-dimensional Boolean operations. This extraction approach maintains the necessary precision for tooth profile definition while dramatically reducing computational complexity.
3Productivity
If image edge tracking method is used, then calculation efficiency is improved, but theoretical error between tracked pixel point and theoretical curve increases
Solution Approach 1:
The patent employs feedback mechanisms through coordinate transformation and calibration processes. By establishing transformation relationships between different engagement states and calibrating the image coordinate system, the system continuously corrects and refines the extracted tooth profile data, reducing the theoretical error between pixel points and actual curves.
Solution Approach 2:
The patent changes the parameter representation from direct pixel coordinates to transformed coordinate systems that account for geometric relationships. By applying coordinate transformations and calibration parameters, the system adjusts the raw image data to achieve higher accuracy while maintaining the computational efficiency of image processing.
Data Source
AI summary
A method for extracting a gear tooth profile edge based on an engagement-pixel image edge tracking method includes defining a transmission ratio relationship between a cutter and an envelope tooth profile, setting a cutter profile step size and an envelope step size, acquiring instantaneous contact images at different engaging times, and performing a binarization processing on each curve envelope cluster image; sweeping a boundary of an envelope curve cluster, acquiring pixel points of the edge; preliminarily tracking a tooth profile edge, and then performing a secondary extraction and compensation on the pixel points; calibrating coordinates of a cutter profile; extracting a pixel coordinate of an instantaneous engaging point; converting the pixel points among different instantaneous engaging images; extracting a final tooth profile coordinate of the gear, and performing a tooth shape error analysis and a contact line error analysis.