Fei Sun

We attempted to improve the strength–ductility balance in tempered martensite steel using multi-objective Bayesian optimization. The martensite steels were tempered at two stages, and fine and coarse cementite particles were mixed. Water-quenching rather than furnace-cooling between the first and second temper stages provided a better balance of strength and ductility. Moreover, the strength–ductility balance was also improved by tempering at a low temperature in the first stage and a high temperature in the second stage, rather than tempering at a high temperature in the first stage and a low temperature in the second stage. Based on these experimental results, multi-objective Bayesian optimization was used to further improve both tensile strength and total elongation. The strength–ductility balance that is better than the experimental results was achieved with a minimal number of optimization times. Additionally, machine learning suggested that it is crucial to control the average aspect ratio of cementite particles to less than 1.8 and the size of coarse cementite particles to less than 2.0 μm in order to improve the strength–ductility balance.

We attempted to improve the strength–ductility balance in tempered martensite steel using multi-objective Bayesian optimization. The martensite steels were tempered at two stages, and fine and coarse cementite particles were mixed. Water-quenching rather than furnace-cooling between the first and second temper stages provided a better balance of strength and ductility. Moreover, the strength–ductility balance was also improved by tempering at a low temperature in the first stage and a high temperature in the second stage, rather than tempering at a high temperature in the first stage and a low temperature in the second stage. Based on these experimental results, multi-objective Bayesian optimization was used to further improve both tensile strength and total elongation. The strength–ductility balance that is better than the experimental results was achieved with a minimal number of optimization times. Additionally, machine learning suggested that it is crucial to control the average aspect ratio of cementite particles to less than 1.8 and the size of coarse cementite particles to less than 2.0 μm in order to improve the strength–ductility balance.

Laser powder bed fusion (LPBF), a typical metal additive manufacturing (MAM) process, generates large thermal gradients and rapid cooling rates, making it challenging to fully understand the relationship between nano-scaled solidification microstructure and process parameters. This study aims to uncover the nano-scaled solidification microstructure characteristics of 316L stainless steel (SS) produced through LPBF. A deeper understanding of microstructural development is crucial for fabricating high-performance materials by effectively controlling the AM process. 1. Introduction Laser powder bed fusion (LPBF) is currently the most versatile metal additive manufacturing (MAM) process, enabling the production of complex parts without the design limitations of traditional manufacturing methods. The rapid and repeated thermal cycles create intense thermal gradients, leading to metastable chemical, structural, and mechanical states, resulting in metallurgical defects that compromise material properties. Extensive research of LPBF-processed 316L SS has been conducted to improve the mechanical properties and highlight the need to correlate different microstructural features with the manufacturing process. Most studies have focused on characterizing microstructures from macro to sub-micrometer scales, with few investigating features at the nano or atomic levels. Therefore, this study aims to explore these nano/atomic scale microstructural features to better understand the relationship between solidification microstructure and process parameters. 2. Experiment The nominal composition of the 316L SS powder was 18Cr-14Ni-2.5Mo-0.03C (wt.%), and the powder size was under 53µm. LPBF fabrication was conducted using a 3D printer (EOSM290, EOS GmbH, Germany) equipped with a Yb-fiber laser by the “X-scan strategy,” i.e., the laser beam was scanned bidirectionally along the X-axis without rotation.

We performed a machine learning-aided analysis of the rolling and recrystallization textures in pure iron with different cold reduction ratios and cold-rolling directions. Five types of specimens with different cold reduction ratios and cold-rolling directions were prepared. The effect of two-way cold-rolling on the rolling texture was small at cold reduction ratios different from 60%. The cold reduction ratio in each stage hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. In the case of long-term annealing, although abnormal grain growth occurred, the crystal orientation of the grains varied. Moreover, the direction of cold-rolling in each stage also hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. During long-term annealing, sheets with the same cold-rolling direction in the as-received state and in the first stage showed the texture evolution of conventional one-way cold-rolled pure iron. Additionally, we conducted a machine learning-aided analysis of rolling and recrystallization textures. Using cold-rolling and annealing conditions as the input data and the degree of Goss orientation development as the output data, we constructed high-accuracy regression models using artificial neural networks and XGBoost. We also revealed that the annealing temperature is the dominant factor in the nucleation of Goss grains.

Sub-micro dislocation cellular structures formed during rapid solidification break the strength–ductility trade-off in laser powder bed fusion (LPBF)-processed 316L stainless steel through high-density dislocations and segregated elements or precipitates at the cellular boundaries. The high-density dislocation entangled at the cellular boundary accommodates solidification strains among the cellular structures and cooling stresses through elastoplastic deformation. Columnar grains with cellular structures typically form along the direction of thermal flux. However, the ultra-low misorientations between the adjacent cellular structures and their interactions with the cellular boundary formation remain unclear. In this study, we revealed the ultra-low misorientations between the cellular structures in LPBF-processed 316L stainless steel using conventional electron backscatter diffraction (EBSD), transmission Kikuchi diffraction (TKD), and transmission electron microscopy (TEM). The conventional EBSD and TKD analysis results could provide misorientation angles smaller than 2°, while the resolution mainly depends on the specimen quality and scanning step size, and so on. A TEM technique with higher spatial resolution provides accurate information between adjacent dislocation cells with misorientation angles smaller than 1°. This study presents evidence that the TEM method is the better and more precise analytical method for the misorientation measurement of the cellular structures and provides insights into measuring the small misorientation angles between adjacent dislocation cells and nanograins in nanostructured metals and alloys with ultrafine-grained microstructures.

Sub-micro dislocation cellular structures formed during rapid solidification break the strength-ductility trade-off in laser powder bed fusion (LPBF)-processed 316L stainless steel through high-density dislocations and segregated elements or precipitates at the cellular boundaries. The high-density dislocation entangled at the cellular boundary accommodates solidification strains among the cellular structures and cooling stresses through elastoplastic deformation. Columnar grains with cellular structures typically form along the direction of thermal flux. However, the ultra-low misorientations between the adjacent cellular structures and their interactions with the cellular boundary formation remain unclear. In this study, we revealed the ultra-low misorientations between the cellular structures in LPBF-processed 316L stainless steel using conventional electron backscatter diffraction (EBSD), transmission Kikuchi diffraction (TKD), and transmission electron microscopy (TEM). The conventional EBSD and TKD analysis results could provide the misorientation angles smaller than 2° while the resolution mainly depends on the specimen quality and scanning step size, and so on. TEM technique with higher spatial resolution provides accurate information between adjacent dislocation cells with misorientation angles smaller than 1°. This study presents that TEM method is the better and more precise analysis method of the misorientation measurement of the cellular structures and provides insights into measuring the small misorientation angles between adjacent dislocation cells and nanograins in nanostructured-metals and alloys with ultrafine-grained microstructures.

The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the effects of microstructure, precipitation, and heat treatment conditions on abnormal grain growth. We simulated abnormal grain growth using the cellular automaton (CA) method. The simulations focused on the grain boundary anisotropy and dispersion of precipitates. We considered the effect of grain boundary misorientation on boundary energy and mobility. The dispersion state of the precipitates and its pinning effect were considered, and grain growth simulations were performed. The results showed that the CA simulation reproduced abnormal grain growth by emphasizing the grain boundary mobility and the influence of the dispersion state of the precipitate on the occurrence of abnormal grain growth. The study findings show that the CA method is a potential technique for the prediction of abnormal grain growth.

Dual-phase (DP) steel has been widely used in automotive steel plates with a balance of excellent strength and ductility. Grain refinement in DP steel is important to improve the properties further; however, the factors affecting grain growth need to be well understood. The remaining problem is that acquiring data through experiments is still time-consuming and difficult to evaluate quantitatively. With the development of materials informatics in recent years, material development time and costs are expected to be significantly reduced through experimentation, simulation, and machine learning. In this study, grain growth behavior in DP steel was studied using two-dimensional (2D) and three-dimensional (3D) Monte Carlo modeling and simulation to estimate the effect of some key parameters. Grain growth can be suppressed when the grain boundary energy is greater than the phase boundary energy. When the volume fractions of the matrix and the second phase were equal, the suppression of grain growth became obvious. The long-distance diffuse frequency can promote grain growth significantly. The simulation results allow us to better understand the factors affecting grain growth behavior in DP steel. Machine learning was performed to conduct a sensitivity analysis of the affecting parameters and estimate the magnitude of each parameter’s effects on grain growth in the model. Combining MC simulation and machine learning will provide one promising research strategy to gain deeper insights into grain growth behaviors in metallic materials and accelerate the research process.