Abnormal growth of austenite grain in continuously cast slab during hot charging processes
Source abstract
Abstract
The hot charging(HC) experiments for 20CrMo steel were carried out based on the temperature history in the plant, and the evolution of austenite grain structure(AGS) was investigated by optical microscope, scanning electron microscope, transmission electron microscope and energy-dispersive spectroscope. A non-monotonic relationship was revealed between the austenite grain size and HC temperature. With decreasing charging temperature, the refinement of AGS after reverse transformation is more obvious owing to the increase in nucleation sites, depending on the amount of prior pearlite fraction before HC. The mixed AGS was firstly reported in the reversed matrix from charging below the finish temperature of austenite decomposition due to the happening of abnormal grain growth. Based on the results from mathematical modeling and precipitate observation, the abnormal grain growth is considered as the result of the coarsening of large grains in the intra-dendritic region and the powerful pinning effect of TiN particles in the inter-dendritic regions. The total number of coarse grains in the reversed matrix is composed of the new large grains after transformation and the untransformed austenite before HC. The disappearance of the untransformed austenite promotes the expanding of the new large grain and the formation of extremely coarse grain, and the total number of coarse grain decreases, leading to the uncommon increase in grain size and the appearance of mixed AGS when charging below 450 °C. For the steels with high sensitivity to abnormal grain growth, the HC temperature is suggested to be slightly higher than the finish temperature of austenite decomposition.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Metallurgical Processes and Thermodynamics · Metal Alloys Wear and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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