Implemented an AI-driven quality control platform using IoT and machine learning to detect defects in real time, improving production efficiency and product quality at scale.
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The challenge
The organisation needed to implement real-time defect detection within a high-volume manufacturing environment, specifically identifying issues in raw materials used in production. Achieving a minimum accuracy threshold while operating with low-cost hardware and on-premises constraints added complexity. Manual inspection processes were inefficient and unable to scale with production volumes, impacting overall product quality and operational performance.
The approach
Designed and deployed an AI-powered solution integrating IoT sensors and machine learning models to monitor production lines in real time. The platform enabled automated data collection and processing, transforming raw production data into actionable insights. Machine learning models were trained to detect defects with high accuracy, while the architecture supported efficient data pipelines and on-premises deployment requirements. The solution combined edge processing with scalable analytics to ensure responsiveness and reliability.
The outcome
Delivered a high-performance quality control solution that significantly improved production efficiency and final product quality. The platform achieved over 94% accuracy and 98% precision in defect detection, enabling more consistent manufacturing outcomes. Real-time monitoring reduced reliance on manual inspection, optimised production processes, and provided a scalable foundation for further industrial AI adoption.
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