Lü Yong, Jia Yunhai, Wang Xuehua, Cheng Peifeng, Zhang Xiaofen Zhang Haoren, Sheng Liang
The size,type,quantity,and spatial distribution of inclusions are core factors determining the fatigue life of high-end metallic structural components.The traditional small-area metallographic methods can hardly reflect the inclusion heterogeneity across the full-section of large forgings,and are prone to miss the local high-risk regions.In this study,targeting aviation-grade 300M steel forgings,a large size metallographic specimen was prepared along the longitudinal section,and optical images were automatically acquired using a 24-unit microscopic array imaging system.To construct a high-precision recognition model,the inclusion types were first labeled on small size metallographic samples by scanning electron microscope and energy dispersive spectroscope(SEM/EDS),and a training dataset was built in combination with data augmentation.Based on the YOLOv11 framework,an inclusion detection model was trained using a transfer learning coupled with incremental optimization strategy,and the recognition results were verified by in-situ compositional validation via laser-induced breakdown spectroscopy(LIBS).The accuracy was approximately 93%. The ultra-large size microscopic array analysis technique was applied to perform fully automatic optical microscopic imaging(25 600 images),machine learning recognition,and laser spectral verification on the longitudinal section of the aviation 300M steel forging (442 mm×221 mm,approximately 97 682 mm2).A total of 29 943 non-metallic inclusions were identified,of which 89.784% were concentrated in the size range of [10,30) μm,and only 4 inclusions(0.013%) were larger than 50 μm.The inclusions were predominantly type A sulfides (22 334 inclusions,74.59%),while type B alumina inclusions were the fewest(474 inclusions,1.58%).A total of 182 995 fields were rated according to method D of ASTM E45,revealing 7 fields with a severity level of 1.0 for the type A heavy series inclusions,2 fields with a severity level of 1.0 for both type C and type D heavy series inclusions.This paper revealed the spatial heterogeneity of inclusions in large forgings,identified localized high-density type A sulfide inclusions regions as major quality risk points,and proposed process optimization suggestions.This study provided a new technical approach for the full-section characterization of inclusions in high-reliability large metal components.
