Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes.

Area of Science:

• Mechanical Engineering
• Electrical Engineering
• Computer Vision

Background:

• Conductive slip rings are vital for power/signal transmission in rotating systems.
• Traditional angle measurement methods lack accuracy and speed for automated production.
• Existing methods struggle with timely data processing and precise control.

Purpose of the Study:

• To develop a machine vision-based method for precise measurement and control of conductive slip ring brush angles.
• To address the limitations of traditional measurement techniques in automated production.
• To improve the contact performance between brush wires and the ring body.

Main Methods:

• A brush angle forming device was constructed to study brush wire forming and rebound characteristics.
• Machine vision and image processing algorithms were employed to measure key brush components.
• A pre-compensation model for brush filament rebound was developed using curve fitting.

Main Results:

• Angle measurement errors were found to be within 0.05°.
• The pre-compensation model demonstrated an average error of 0.112° for rebound angles.
• The study successfully validated the accuracy and effectiveness of the developed model.

Conclusions:

• The machine vision method enables precise control of brush angles, enhancing conductive slip ring performance.
• The pre-compensation model accurately predicts brush filament rebound, improving measurement reliability.
• This approach offers potential for broader engineering applications in automated inspection and control.