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Related Experiment Video

Updated: Jun 18, 2026

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

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

Published on: August 29, 2025

Deep learning enabled intelligent robotic system for aeroengine blade surface inspection.

Ehtesham Iqbal¹, Abdelrahman Youssef¹, Samee Ullah Khan¹

¹Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

Scientific Reports

|June 16, 2026

Summary

This summary is machine-generated.

This study introduces an intelligent robotic system using deep learning for automated aeroengine blade (AEB) inspection. The system significantly reduces inspection time and improves defect detection accuracy, enhancing aerospace maintenance, repair, and overhaul (MRO) processes.

Related Experiment Videos

Last Updated: Jun 18, 2026

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

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

Published on: August 29, 2025

Area of Science:

Robotics and Automation
Artificial Intelligence
Aerospace Engineering

Background:

Aeroengine blades (AEBs) are critical aircraft components requiring rigorous inspection for safety.
Traditional inspection methods (manual, borescope) are inefficient, time-consuming, and prone to human error.
Aerospace Maintenance, Repair, and Overhaul (MRO) demands improved inspection solutions.

Purpose of the Study:

To develop and validate a deep learning-enabled intelligent robotic system for autonomous AEB inspection.
To address the limitations of conventional inspection techniques in aerospace MRO.
To enhance the efficiency and accuracy of AEB surface defect detection.

Main Methods:

Collected two datasets for aeroengine blade localization and surface defect detection.
Trained robust deep learning models for each specific task.
Integrated trained models into an intelligent robotic system for automated workflow.
Implemented real-time autonomous blade localization, defect detection, and return.

Main Results:

The system achieved high accuracy in both localization and defect detection tasks.
Achieved an mAP (mean Average Precision) of 88.2% for defect detection.
Reduced inspection cycle time to approximately 4 seconds per blade.
Significantly improved efficiency and accuracy compared to traditional methods.

Conclusions:

The proposed deep learning-enabled robotic system offers a reliable and efficient solution for AEB inspection in industrial MRO.
Seamlessly combines vision-based deep learning with robotic automation for enhanced aerospace maintenance.
Overcomes limitations of manual inspection, improving airworthiness and operational safety.