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Investigation of Computer Vision and Machine Learning to Enhance Quality Control Processes in Aerospace Manufacturing: Innovative Machine Learning Applications in the Aerospace Industry
Abstract
The integration of computer vision and machine learning (ML) in aerospace manufacturing has revolutionized quality control processes by enabling automated defect detection, predictive maintenance, and real-time monitoring. Traditional quality inspection methods often involve manual assessments, Computer vision systems, powered by deep learning algorithms, enhance defect detection accuracy by analysing images and sensor data, Machine learning further optimizes quality control by predicting potential failures through data-driven insights, reducing production downtime, and improving overall efficiency. This paper explores recent advancements in computer vision and ML for aerospace manufacturing, highlighting their impact on defect classification, anomaly detection, and process optimization. The study also discusses challenges such as data availability, model interpretability, and computational resource requirements. By leveraging intelligent automation, aerospace manufacturers can achieve higher reliability, cost efficiency, and compliance with stringent industry standards.
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