ArticleScientific reports2026
Deep learning enabled intelligent robotic system for aeroengine blade surface inspection.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aeroengine blades (AEBs) are critical components of aircraft engines that require consistent monitoring and inspection to ensure airworthiness and operational safety. The aerospace industry relies heavily on maintenance, repair, and overhaul (MRO) procedures, where surface inspection of AEBs plays a pivotal role. Traditional manual or borescope-based inspection methods are time consuming, labor intensive, and susceptible to human error. In this work, a deep learning enabled intelligent robotic system is proposed for the autonomous inspection of AEBs, addressing key MRO requirements. Two distinct datasets were collected, one for aeroengine blade localization and another for surface defect detection. A robust deep learning model is trained separately on each dataset to perform their respective tasks with high accuracy. The trained models were integrated into an intelligent robotic system to automate the inspection workflow. In real time operation, a container filled with aeroengine blades is placed in front of the intelligent system, which autonomously localizes each blade, picks it up, places it in an image acquisition box, detects any surface defects, and returns the blade to its original position. The system provides real time feedback and accelerates decision making processes. Experimental results demonstrate that the proposed approach significantly reduces inspection time and enhances defect detection accuracy compared to conventional methods. By seamlessly combining vision based deep learning with robotic automation, the system offers a reliable solution for controlled industrial inspection environments for modern aerospace MRO processes, overcoming the limitations associated with manual inspection techniques. Experimental results demonstrate that the proposed approach achieves an mAP of 88.2% and reduces inspection cycle time to approximately 4 seconds per blade, significantly improving efficiency compared to conventional methods.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.