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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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Machine vision-based angle-arrayed imaging and two-stage deep learning for gear defect detection.

Jianxi Li, Zhanwei Liu, Xianfu Huang

|March 17, 2026

Summary

This summary is machine-generated.

This study introduces an advanced online method for detecting industrial gear surface defects using in situ imaging and a two-stage deep learning approach. The system ensures accurate defect identification, enhancing machinery reliability and operational safety.

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Area of Science:

Mechanical Engineering
Artificial Intelligence
Materials Science

Background:

Industrial gears face significant challenges with surface defects under demanding operational conditions, impacting service life and machine integrity.
Existing online defect detection methods struggle with precision in complex industrial environments.

Purpose of the Study:

To develop a high-precision, online method for detecting surface defects on industrial gears.
To improve the reliability and operational safety of machinery through advanced quality monitoring.

Main Methods:

Integration of synchronous in situ tooth surface imaging with a two-stage deep segmentation strategy.
Utilized motion-aligned imaging for complete tooth surface coverage.
Employed a cascaded architecture with an improved U-shaped dual-resolution network (UDDRNet) for precise defect identification.

Main Results:

Achieved 87.42% mean Intersection over Union (mIoU), 91.89% Recall, and 92.15% F1 scores.
Demonstrated superior performance compared to single-stage methods and existing semantic segmentation models.
Maintained real-time detection capabilities suitable for industrial applications.

Conclusions:

The proposed method offers high accuracy and practicality for online defect detection in complex industrial gear scenarios.
Provides a scalable technical solution for intelligent quality monitoring of critical industrial components.
Enhances the potential for predictive maintenance and reduces unexpected machinery failures.