Evaluation of AI-solution for Microstructural Analysis of Steel

Sammanfattning

Manual microstructural analysis of high-strength steel wire is both time-consuming and prone to subjectivity, creating challenges for consistent quality control in industrial production. This study evaluates the feasibility of integrating AI-based image analysis into the metallographic workflow at Suzuki Garphyttan AB, using the Olympus PRECiV software. Both pre-trained integrated AI models and custom-trained deep learning models were assessed against manual baseline measurements for grain size (ASTM E112) and oxide layer thickness (ASTM B487) across five specimens each. Additionally, a standardised image acquisition protocol was developed for the Olympus DSX 1000 3D-microscope. Results demonstrate that all three methods produced grain size numbers in close agreement across all specimens, indicating promising accuracy for industrial use. Oxide layer thickness results showed good overall agreement, though specimens with irregular oxide morphology presented greater divergence between methods. The integrated AI method is the most immediately deployable solution, offering denser sampling and grain size distributions beyond what manual methods provide. The findings support a semi-automated workflow as a viable complement to manual metallographic analysis.