Strength improvement over 2 GPa and austenite grain ultra-refinement in a low carbon martensite steel achieved by ultra-rapid heating and quenching

This study focuses on enhancing the strength of low-carbon martensitic steel through the refinement of the prior austenite grain size. After subjecting the steel to heavy cold deformation, ultra-rapid heating in a salt bath was employed. The steel was then quenched in water. The results showed that the ultra-rapid heating for 5 s achieved an impressive tensile strength exceeding 2 GPa, accompanied by a higher hardness of over 500 kgf/mm2. By introducing more nucleation sites through heavy cold rolling, it was possible to achieve an ultrafine-grain structure in the steel, with the prior austenite grain size ranging from 1∼3 μm. In some regions, even smaller grain sizes of around 1 μm or less were observed. The boundaries between martensite packets or blocks disappeared, replaced by two or three lath variations. The study also investigated the effect of high-temperature holding time on the austenite phase. It was found that increasing the holding time resulted in the growth of the austenite grains and a subsequent decrease in tensile strength. Considering carbide dissolution, the optimal temperature holding time was approximately 5 s at 900 °C. A shorter holding time allowed more carbides to remain, which weakened the strengthening effect of the carbon solid solution. The relationship between strength and grain size was analyzed by fitting experimental data. The Hall-Petch slopes were calculated to be about 862 MPa mm1/2 and 1022 MPa mm1/2 for the prior austenite grain size and the effective grain size, respectively. Moreover, excluding the influence of martensite packet or block boundaries, the strength contributions from the carbon solid solution and dislocation strengthening were determined to be approximately 583 MPa and 268 MPa, respectively

Dimensionality reduction for machine learning using statistical methods: A case study on predicting mechanical properties of steels

Steel manufacturing is a long and complicated process including refining, casting, and rolling; hundreds of process parameters can potentially influence the mechanical properties of final products. This complexity results in significant challenges in correlating input parameters with final mechanical properties. Machine learning models, neural networks and XGBoost, have been used in the prediction of mechanical properties, however, interpretability remains an issue, especially in the case of neural networks. In this study, a statistical method - iGATE is utilised to reduce dimension of inputs in predicting mechanical properties of hot-rolled steel plates. It is found that iGATE can successfully extract the key features and reduce the dimension of inputs while maintaining a high prediction accuracy. With relative errors lower than 5 %, XGboost with full inputs has the best prediction performance. With reduced input dimensions, interference of irrelevant features diminishes, and the ranking of important key features is more reliable. The iGATE methodology offers industry opportunities to identify the key input parameters in terms of materials chemistry and process variables to optimise mechanical properties of rolled plates

Metallurgical Data Science for Steel Industry: A Case Study on Basic Oxygen Furnace

The steel industry has developed sensorization to generate data, monitoring systems, and steelmaking process control. The remaining challenges are data storage issues, lack of cross-production data links, and erroneous datasets, which significantly increase the quality control complexity. The development of a data-driven approach through artificial intelligence (AI) techniques enables machine learning techniques to big datasets aiming to provide process–property optimization and identify challenges and gaps in the data. Recently, computational capabilities and algorithmic developments have significantly grown in power and complexity, accelerating process optimization. Addressing large-scale industrial data process–property optimization strategies involve numerous influencing possessing factors but limited data. As one of the largest production chains in the world, the steel industry faces an ever-increasing demand for larger components, high levels of functionality, and quality of the final product. Herein, an integrated data-driven steelmaking case study is built with the aim of predicting and optimizing the final product composition and quality. Machine learning is used collaboratively with fundamental knowledge, first-principal calculation, and feedback into a backpropagation neural network (NN) model. Integrating data mining and machine learning generates reasonable predictions and addresses process efficiencies within the steelmaking furnaces. The ultimate goal is to enhance the digitalization of the steel industry.</p