Sliding wear of medium-carbon bainitic/martensitic/austenitic steel treated by short-term low-temperature austempering

A medium-carbon Si–Mn–Ni–Cr–Mo alloyed (300M) steel was austempered for various short periods at its martensite-starting temperature of 285 °C to seek improved sliding wear resistance as compared to the traditional martensitic and bainitic steels. Reciprocating sliding wear tests were performed against a WC/Co ball counterpart at a constant load of 49 N. The samples were characterised using field emission SEM, XRD and hardness testing. The associated wear mechanisms were analysed using SEM and cross-sectional TEM. The results revealed that a short austempering time of 6 min produced refined arrays of initial nano-bainitic ferrite laths and inter-lath filmy austenite and the majority martensite and retained austenite, while the majority of the microstructure remained martensitic with retained austenite. The hardness was unchanged to that of the as-quenched martensite of 6.4 GPa. Simultaneously the wear coefficient decreased by 41% from 2.67 to 1.58 × 10-15 m3N-1m-1, which is also superior to both the tempered martensite at 1.65 × 10-15 m3N-1m-1 and the lower bainite at 1.87 × 10-15 m3N-1m-1. Increasing the austempering time to 20 and 60 min resulted in wear coefficients of 1.38 and 2.18 × 10-15 m3N-1m-1, respectively. The improved wear resistance has been explained by the wear induced microstructure evolution, especially the carbon partitioning induced stabilisation of retained austenite. The high-stress sliding wear was found to be dominated by severe shear deformation, which resulted in a nano-laminate structured top layer. Delamination wear was found to take place within the embrittled nano-laminates

Expert recommendations on collection and annotation of otoscopy images for intelligent medicine

Middle and outer ear diseases are common otological diseases worldwide. Otoscopy and otoendoscopy examinations are essential first steps in the evaluation of patients with otological diseases. Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy, leading to delays in treatment or complications. Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future. However, the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems, and no standardized process for data acquisition, and annotation of otoscopy images in intelligent medicine has yet been fully established. The standards for data storage and data management are unified with those of other specialties and are introduced in detail here. This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine; it would thus lay a solid foundation for the standardized collection, storage, and annotation of otoscopy images and the application of training algorithms, and promote the development of automatic diagnosis and treatment for otological diseases. The full text introduced image collection (including patient preparation, equipment standards, and image storage), image annotation standards, and quality control