Zhou Wang

The oxidation of 3Cr3Mo2NiW and 3CrNi3Mo steels was studied at 600 °C in air, and the test results suggest that the parabolic rate law fitted the oxidation kinetics of both steels. The microstructure, morphology, structure, and phase composition of the oxide film cross-sectional layers of the two Cr-Ni-Mo hot-work die steels were analyzed using scanning electron microscopy (SEM), energy-dispersive spectrometry (EDS), and X-ray diffraction (XRD). The influences of Cr, Ni, and Mo on the high-temperature oxidation resistance of the two Cr-Ni-Mo hot-work die steels are discussed, and the oxidation mechanism is summarized. Heat-treated samples were analyzed using electron backscattered diffraction (EBSD) to obtain inverse pole figures (IPFs) and average sample grain sizes, and the percentages of twin grain boundaries (TGBs) (θ = 60°) were also measured. After heat treatment, recrystallization was observed in both steels with a large portion of twin grain boundaries. After 10 h of oxidation, the dense chromium-rich oxide layer that formed in the inner oxide layer of 3Cr3Mo2NiW steel effectively prevented the continuation of oxidation. The inner oxide layer in 3CrNi3Mo steel formed an adhesion layer with a network structure composed mainly of Ni- and Cr-rich spinel oxide, without forming a barrier to prevent oxidation.

The precise prediction of end-point carbon content in liquid steel plays a critical role in increasing productivity as well as energy efficiency that can be achieved in the basic oxygen furnace (BOF) steelmaking process. Due to numerous and diversity of the studies on BOF end-point carbon prediction, it seems necessary to provide a comprehensive literature review on state-of-the-art developments in end-point carbon prediction for BOF steelmaking. This paper presents the characteristics of different end-point carbon prediction models. The end-point carbon prediction for BOF steelmaking has initially relied on the experience and skill of the operators. With the development of information technology and auto-detection methods, BOF end-point carbon prediction mainly has gone through three stages, such as static prediction, dynamic prediction, and intelligent prediction. Future contributions to the development and application of intelligent end-point carbon prediction in BOF steelmaking are still arduous tasks. However, it is envisaged that the intelligent end-point carbon prediction will witness more frequent applications and greatly improve the high-quality, high-efficiency, and stable production for BOF steelmaking in the future.