Extruded Tube Surface Defect Detection Using Machine Learning
Hydro Norsk, a leading company in the steel industry with worldwide
operations and factories, faced a critical concern of early defect detection
in their production pipeline. The presence of even minor defects resulted in
significant waste of raw materials. Although Hydro had an existing solution
based on real-time image collection of their production pipelines, they
identified the potential for improvement using contemporary techniques.
Consequently, they approached our team to investigate and explore new
By leveraging our deep experience in AI-powered image processing,
we started to design and develop an algorithm capable of accurately identifying
the various types of defects in their product.
Deploying the solution in the initial pilot factory introduced a new set of challenges.The data processing pipeline needed to be exceptionally fast, making intelligent decisions about which data were worth analyzing to enable prompt defect detection. This was crucial to ensure swift actions could be taken when necessary. The data processing pipeline also needed to be resilient to failures, ensuring it continued to function even in the face of unexpected glitches or issues. Additionally, the output of the data processing pipeline needed to be readily accessible via a real-time dashboard. This dashboard would highlight problematic patterns in the production lines, providing insight into deeper issues that required attention.
Our collaboration with Hydro resulted in a substantial enhancement
of Hydro's defect detection capabilities. The project's success can be
attributed to the combination of Hydro's industry expertise and our proficiency
in AI, image processing techniques and interactive dashboard creation.