Volume 62, Issue 3

Pursuing the reduction of CO 2 emissions, steel industry shifts towards a scrap-based electric arc furnace production that results in elevated levels of tramp elements. The effect of these elements on material properties and microstructure is not understood in detail in advanced microstructural complex steels. This study employs methods to investigate the microstructure of a typical cold headed wire-rod steel, micro-alloyed with vanadium, from blast furnace production and a trial alloy with intentionally increased tramp element concentrations. Light optical microscopy is performed to determine grain sizes of the ferritic-pearlitic microstructure and their phase fractions. The interlamellar spacing of pearlite is explored with scanning electron microscopy. Additionally, the regularity of pearlite lamellae is assessed with a machine learning approach. Thermally etched austenite grains are observed with a high-temperature laser scanning confocal microscope at 1000 °C. A comparison to recalculated prior austenite grains from electron backscatter diffraction patterns is performed. The microstructure after cooling is analyzed by LePera etching and phase maps are obtained by electron back scatter diffraction. The results show that tramp elements do not have an influence on the grain size and phase fractions of the ferritic-pearlitic microstructure in the studied alloy. However, it seems that regular growth of the lamellae is impeded in the trial alloy. Moreover, smaller prior austenite grain sizes are observed and displacive constituents are significantly pronounced, if tramp elements are increased in the alloy.

The determination of average grain size is an important component in the microstructural characterization of metallic materials. The grain size is usually determined using the intercept and comparison methods, but there are problems with the relatively time-consuming intercept method and the low accuracy of the comparison method. In this paper, a new model is proposed to realize the task of automatic grain size grading using a combination of traditional image processing and deep learning. Deep learning semantic segmentation and image classification are used to achieve the grading of different quality images. The model is capable of extracting and complementing grain boundaries and calculating the pixel area of the grains. The accuracy of the model was tested on metallographic phases of pure iron and austenitic stainless steel. Using this model, grain size comparison charts were produced for ISO 643 based on real metallographic photographs.

Due to the large number of consumables available, the flexibility required to achieve appropriate properties in the welded joint is very high. Fundamental knowledge about development of different electrodes for structural steel, of which information is scarce in international welding literature. In present work six rutile coated electrodes were prepared by increasing calcite content, at the expense of cellulose and Si-bearing components like mica and china clay, in the fluxes. This modification produced an increase in the slag basicity, which caused a marked increment in all-weld-metal toughness and modifications in operational behavior with a decrease in penetration and width of the weld bead, while maintaining the typical excellent operational characteristics of rutile electrodes. Microstructure examination of six electrodes indicates the presence of inclusions and secondary phase particles. Maximum tensile strength of 430 N/mm 2 while minimum tensile strength of 323 N/mm 2 was observed for 7.5 % and 12.5 % calcite content. In F position, 7.5 %, 10 %, 12.5 % calcite was faster than 15 %, 17.5 % and 20 % calcite. For DC (+), 15 %, 17.5 %, 20 % calcite presented higher spatter than 5 %, 10 %, 12.5 % calcite.

After several combustion tests with natural gas-hydrogen mixtures, a fracture occurred inside the compression fitting during disassembly of a thin-walled ¼-inch tube due to high-cycle fatigue fracture. There was no evidence of hydrogen embrittlement or other damage mechanisms.