Örebro researchers Rajesh Patil and Professor Magnus Löfstrand have developed an AI system that detects welding defects, reduces material waste, and supports sustainable manufacturing.
“We’re now looking to test the system together with the automotive industry,” says Rajesh Patil.
Rajesh Patil and Professor Magnus Löfstrand envision manufacturing plants where machines, without human involvement, rapidly identify welding defects, remove faulty components, and minimise material waste. Their research is conducted in mechanical engineering at Örebro University’s School of Science and Technology.
Together, they have developed an AI-driven inspection system that detects and classifies weld defects in engine exhaust components with high speed and precision. By combining artificial neural networks (ANNs) with support vector machines (SVMs), the system can identify defects in both similar and dissimilar metals – tasks that would be both time-consuming and extremely difficult for human inspectors.
“Our AI acts as smart eyes on the factory floor, identifying welding defects in real time, improving product quality, reducing waste, and enabling faster, more sustainable production,” says Magnus Löfstrand.
The study, which is being published in a scientific journal, builds on previous research in AI-based weld inspection and has resulted in a fully autonomous system adaptable to various industrial welding applications. The method can help reduce both energy consumption and material waste, aligning with Sweden’s goals for sustainable manufacturing.
“This is a major step towards smart factories where AI works side by side with people to improve efficiency and sustainability,” says Rajesh Patil.
The research group is now seeking collaboration with local and international automotive manufacturers to test the system in industrial production environments.
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