Effect of Cold Rolling Reduction Rate on the Microstructure and Properties of Q&P Steel with a Ferrite-Pearlite Initial Structure.

Quenching and partitioning (Q&P) steel has garnered attention as a promising third-generation automotive steel. While the conventional production (CP) method for Q&P steel involves a significant cumulative cold rolling reduction rate (CRRR) of 60-70%, the thin slab casting and rolling (TSCR) process has emerged as a potential alternative to reduce or eliminate the need for cold rolling, characterized with a streamline production chain, high-energy efficiency, mitigated CO2 emission and economical cost. However, the effect of the CRRR on the microstructure and properties of Q&P steel with an initial ferrite-pearlite microstructure has been overlooked, preventing the extensive application of TSCR in producing Q&P steel. In this work, investigations involving different degrees of CRRRs reveal a direct relationship between increased reduction and decreased yield strength and plasticity. Notably, changes in the microstructure were observed, including reduced size and proportion of martensite blocks, increased ferrite proportion and decreased retained austenite content. The decrease in yield strength was primarily attributed to the increased proportion of the softer ferrite phase, while the reduction in plasticity was primarily linked to the decrease in retained austenite content. This study provides valuable insights for optimizing the TSCR process of Q&P steel, facilitating its wider adoption in the automotive sector.

Multi-Scale Microstructural Tailoring and Associated Properties of Press-Hardened Steels: A Review.

High-strength press-hardened steels (PHS) are highly desired in the automotive industry to meet the requirement of carbon neutrality. This review aims to provide a systematic study of the relationship between multi-scale microstructural tailoring and the mechanical behavior and other service performance of PHS. It begins with a brief introduction to the background of PHS, followed by an in-depth description of the strategies used to enhance their properties. These strategies are categorized into traditional Mn-B steels and novel PHS. For traditional Mn-B steels, extensive research has verified that the addition of microalloying elements can refine the microstructure of PHS, resulting in improved mechanical properties, hydrogen embrittlement resistance, and other service performance. In the case of novel PHS, recent progress has principally demonstrated that the novel composition of steels coupling with innovative thermomechanical processing can obtain multi-phase structure and superior mechanical properties compared with traditional Mn-B steels, and their effect on oxidation resistance is highlighted. Finally, the review offers an outlook on the future development of PHS from the perspective of academic research and industrial applications.

Study on Corrosion Behavior and Mechanism of Ultrahigh-Strength Hot-Stamping Steel Based on Traditional and Compact Strip-Production Processes.

Hot-stamping steel is a type of high-strength steel that is mainly used in key safety components such as the front and rear bumpers, A-pillars, and B-pillars of vehicles. There are two methods of producing hot-stamping steel, i.e., the traditional process and the near net shape of compact strip production (CSP) process. To assess the potential risks of producing hot-stamping steel using CSP, the microstructure and mechanical properties, and especially the corrosion behavior were focused on between the traditional and CSP processes. The original microstructure of hot-stamping steel produced by the traditional process and the CSP process is different. After quenching, the microstructures transform into full martensite, and their mechanical properties meet the 1500 MPa grade. Corrosion tests showed that the faster the quenching speeds, the smaller the corrosion rate of the steel. The corrosion current density changes from 15 to 8.6 µA·cm-2. The corrosion resistance of hot-stamping steel produced by the CSP process is slightly better than that of traditional processes, mainly since the inclusion size and distribution density of CSP-produced steel were both smaller than those of the traditional process. The reduction of inclusions reduces the number of corrosion sites and improves the corrosion resistance of steel.

The Characteristic Microstructures and Properties of Steel-Based Alloy via Additive Manufacturing.

Differing from metal alloys produced by conventional techniques, metallic products prepared by additive manufacturing experience distinct solidification thermal histories and solid-state phase transformation processes, resulting in unique microstructures and superior performance. This review starts with commonly used additive manufacturing techniques in steel-based alloy and then some typical microstructures produced by metal additive manufacturing technologies with different components and processes are summarized, including porosity, dislocation cells, dendrite structures, residual stress, element segregation, etc. The characteristic microstructures may exert a significant influence on the properties of additively manufactured products, and thus it is important to tune the components and additive manufacturing process parameters to achieve the desired microstructures. Finally, the future development and prospects of additive manufacturing technology in steel are discussed.

Phase Transformation Behaviors of Medium Carbon Steels Produced by Twin Roll Casting and Compact Strip Production Processes.

Medium carbon steels have been widely used in the fields of tool and die manufacturing due to their outstanding hardness and wear resistance. In this study, microstructures of 50# steel strips fabricated by twin roll casting (TRC) and compact strip production (CSP) processes were analyzed to investigate the influences of solidification cooling rate, rolling reduction, and coiling temperature on composition segregation, decarburization, and pearlitic phase transformation. The results show that a partial decarburization layer with a thickness of 13.3 µm and banded C-Mn segregation were observed in the 50# steel produced by CSP, leading to the banded distributions of ferrite and pearlite in the C-Mn poor regions and C-Mn rich regions, respectively. For the steel fabricated by TRC, owing to the sub-rapid solidification cooling rate and short processing time at high temperatures, neither apparent C-Mn segregation nor decarburization was observed. In addition, the steel strip fabricated by TRC has higher pearlite volume fractions, larger pearlite nodule sizes, smaller pearlite colony sizes and interlamellar spacings due to the co-influence of larger prior austenite grain size and lower coiling temperatures. The alleviated segregation, eliminated decarburization and large volume fraction of pearlite render TRC a promising process for medium carbon steel production.

Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model.

Hardenability is one of the most basic criteria influencing the formulation of the heat treatment process and steel selection. Therefore, it is of great engineering value to calculate the hardenability curves rapidly and accurately without resorting to any laborious and costly experiments. However, generating a high-precision computational model for steels with different hardenability remains a challenge. In this study, a combined machine learning (CML) model including k-nearest neighbor and random forest is established to predict the hardenability curves of non-boron steels solely on the basis of chemical compositions: (i) random forest is first applied to classify steel into low- and high-hardenability steel; (ii) k-nearest neighbor and random forest models are then developed to predict the hardenability of low- and high-hardenability steel. Model validation is carried out by calculating and comparing the hardenability curves of five steels using different models. The results reveal that the CML model works well for its distinguished prediction performance with precise classification accuracy (100%), high correlation coefficient (≥0.981), and low mean absolute errors (≤3.6 HRC) and root-mean-square errors (≤3.9 HRC); it performs better than JMatPro and empirical formulas including the ideal critical diameter method and modified nonlinear equation. Therefore, this study demonstrates that the CML model combining material informatics and data-driven machine learning can rapidly and efficiently predict the hardenability curves of non-boron steel, with high prediction accuracy and a wide application range. It can guide process design and machine part selection, reducing the cost of trial and error and accelerating the development of new materials.