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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu
Presentation Title Data-driven Modelling of Metallurgical Processes – A Case Study on BOF Process
Author(s) Hongbiao Dong
On-Site Speaker (Planned) Hongbiao Dong
Abstract Scope Steel manufacturing is a long and complicated process including stages of BF ironmaking, BOF steelmaking, refining, casting and rolling; thousands of processing parameters can potentially influence mechanical properties of final products. Recently, significant progress has been made in steel industry to develop online monitoring systems, collecting data for process control. Challenges remain in the area of data storage, cross-process data links, erroneous datasets, the correlation between chemistry, process variables and mechanical properties. 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. 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 of metallurgy process, and feedback into a backpropagation neural network (NN) model. Integrating data mining and machine learning generates reasonable predictions and addresses process efficiencies within the BOF steelmaking process.
Proceedings Inclusion? Planned:
Keywords Other,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Comprehensive First-principles and Machine Learning Study of Pure Elements and Alloys: From Pure Shear Deformation to Data-driven Insights into Mechanical Properties
A New Modeling Approach for Co-base Superalloys
A Solution to the Temperature Evolution of Multi-well Free-energy
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Additive Manufacturing of Steels – Application of Computational Thermodynamics and Kinetics to Alloy Development
Alloy Design Based on Automated CALPHAD Composition Search and Machine Learning
Applications of the CALPHAD Approach to Nuclear Materials Design
Big Data-Assisted Digital Twins for the Smart Design and Manufacturing of Advanced Materials: From Atoms to Products
CALPHAD-based ICME Design for Additive Manufacturing of Functionally Graded Alloys
CALPHAD Supported by Advanced Materials Analytics
Computational Design of Engineering Materials: Tools and Applications
Computational Design of Novel High-Entropy Alloys: Multi-Strengthening Mechanisms vs Neural Network Model
Coupling Physics in Data-driven High-temperature Alloys Design via High-throughput CALPHAD
Data-Driven Discovery and Design of Thermoelectric Materials
Data-driven Modelling of Metallurgical Processes – A Case Study on BOF Process
Design of Compositional Pathways for Functionally Graded Materials in Additive Manufacturing
Efficient Exploration of Compositionally Complex Alloys
Genomic Materials Design: The Concurrency Frontier
High Temperature Creep Induced Phase Transformation in Austenitic Stainless Steels
M-23: Electronic Origin of Phase Stability in Mg–Zn–Y Alloys with a Long-Period Stacking Order: A First-Principles Study
M-24: Revealing the Materials Genome for Advanced High-entropy Materials
Magnesium & Mentoring - 15 Years of Science and Friendship with Prof. Liu.
Materials Modelling for Metals Processing
Melting Temperature Prediction via Integrated First Principles and Deep Learning
Rapidly Generating Calphad Databases with High-throughput First-principles Calculations
Selected Observations in Magnesium Alloys: From Diffusion Couples to Laser Powder Bed Fusion
Stability of Transition Metal High Entropy Alloys: From First-principles and Machine Learning
The Materials Genome and Cross Effects in Transport Phenomena
The Materials Genome Initiative
The Modern-day Blacksmith
Thermochemical and Thermophysical Properties of Metal Diborides (MB2 | M = Ti, Zr, Nb, Hf, Ta) up to 3150 ˚C
Thermodynamics of Iodine Terminated MXenes from First-principles Calculations and CALPHAD Modeling
Understanding Interstitial and Substitutional Alloying of Refractory Metals
Zentropy

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