High-strength low-alloy steels have been widely used worldwide in offshore structural applications like jack-up rigs and wind turbine installation vessels. With rising demand for materials with higher mechanical strength, the X100Q grade, a C-Mn steel with low carbon content and a well-balanced microalloying concept, has emerged as a promising candidate. In this work, steel bars were hot-rolled to produce seamless pipes, which were then quenched and tempered. Microstructural characterization of the pipe revealed a microstructure consisting of bainite and tempered martensite. Mechanical charac-terization demonstrated that the material has a good combination of high strength and toughness in temperatures as low as –60°C.

Hot ductility testing is a well-established method to investigate effects of alloying and casting conditions on slab surface sensitivity to cracking. In this work, the hot ductility of four different steel chemistries ranging in carbon content from 0.06 to 0.40 wt. % and with varying amounts of precipitate forming elements (e.g., Ti, Nb, V) are studied using a Gleeble 3800. Specimens prepared from continuously cast slabs were homogenized at 1,350°C for 5 minutes before cooling at 1°C/second to testing temperatures between 1,300°C and 600°C and then strained isothermally to failure with a rate of 1 x 10-3 second-1. The reduced area of these tests was used to evaluate their hot ductility performance. Fractography on the tested specimens and metallography on quenched specimens after homogenization were performed. Alongside these experimental results, the Thermo-Calc predicted phase and precipitate stability are presented. Using these results, factors affecting hot ductility performance in these grades are discussed.

Mechanical properties of advanced high-strength steel (AHSS) are linked to its microstructure, influenced by processing techniques during production, specifically by hot rolling thermomechanical processing. In this article, a novel adaptive machine learning (ML) model coupled with controlled cooling of hot-rolled plates was developed to predict bainite in AHSS. A neural network model of the time-temperature-transformation diagram was used at each cooling step to predict continuous-cooling-transformation kinetics. To verify the bainite fraction, dilatometry experiments were performed with AHSS specimens cooled at rates from 0.1 to 10°C/second. An adaptive-ML model for bainite was trained using inputs from experiments and simulation, offering a pre-dictive tool for optimizing AHSS processing.

The article describes the positive experience at Acciaierie Bertoli Safau (ABS) during the development of the whole manu-facturing process (from meltshop to rolling mill) of high-silicon grade 54SiCr6 for automobile suspension springs. The evo-lution of this learning process is presented, with a focus on the implemented aspects (i.e., materials, process parameters, equipment, production scheduling) that proved to be fundamental for the achievement of the product requirements (i.e., steel chemical composition, microinclusion morphology and quantity, blooms and wire rod internal and surface quality). The satisfactory feedback from product performances confirms the effectiveness of the development path and invites future improvements.