首页|New Machine Learning Findings from Fraunhofer Institute of Chemistry Technology Described (Segmentation and Metallographic Evaluation of Aluminium Slurry Coatin gs Using Machine Learning Techniques)
New Machine Learning Findings from Fraunhofer Institute of Chemistry Technology Described (Segmentation and Metallographic Evaluation of Aluminium Slurry Coatin gs Using Machine Learning Techniques)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting fromPfinztal, Germany, by NewsRx journal ists, research stated, “Analysis of scanning electron microscope(SEM) images is crucial for characterising aluminide diffusion coatings deposited via the slurr y route onsteels, yet challenging due to various factors like imaging artefacts , noise, and overlapping features suchas resin, precipitates, cracks, and pores . This study focuses on determining the thicknesses of the coatinglayers Fe2Al5 and, if present, FeAl, pore characteristics, and chromium precipitate fractions after the heattreatment that forms the diffusion coating.”
PfinztalGermanyEuropeCyborgsEmer ging TechnologiesMachine LearningFraunhofer Institute of Chemistry Technolog y
NETL
NSTL
