Papers citing A predicting model for properties of steel using the industrial big data based on machine learning
Classification of Nonmetallic Inclusions in Steel by Data‐Driven Machine Learning Methods
steel research international · 2022-09-29
Shashank Ramesh Babu, Robert Musi, Kathrin Thiele, Susanne Michelic
Read original paper ↗ ·Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset
Materials & Design · 2022-10-18
Chunyuan Cui, Guangming Cao, Yang Cao, Jianjun Liu, Zishuo Dong, Siwei Wu, Zhenyu Liu
Read original paper ↗ ·Design of new Al-Si-Mg alloys by multi-modal mixed input simulation experiment
Acta Physica Sinica · 2022-10-19
Zhiqiang Duan, Xiao-long Pei, Qingwei Guo, Hua Hou, Yuhong Zhao
Read original paper ↗ ·A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels
Journal of Materials Processing Technology · 2022-10-29
Chunyuan Cui, Guangming Cao, Xin Li, Zhiwei Gao, Jianjun Liu, Zhenyu Liu
Read original paper ↗ ·Benchmarking AutoML for regression tasks on small tabular data in materials design
Scientific Reports · 2022-11-11
Felix Conrad, Mauritz Mälzer, Michael Schwarzenberger, Hajo Wiemer, Steffen Ihlenfeldt
Read original paper ↗ ·Process parameters design of squeeze casting through an improved KNN algorithm and existing data
Journal of Manufacturing Processes · 2022-11-14
Jianxin Deng, Bin Xie, Dongdong You, Ling Wang, Xiusong Wu, Gang Liu, Jiawei Liang
Read original paper ↗ ·Ensemble Learning for Predicting the Tensile Strength of Alloy Steels from Chemical Composition and Processing Parameters
International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · 2022-11-16
Hristina N. Kulina, Mariana Koleva-Petrova, Snezhana Georgieva Gocheva-Ilieva
Read original paper ↗ ·Dimensionality reduction for machine learning using statistical methods: A case study on predicting mechanical properties of steels
Materials Today Communications · 2022-12-15
Xiaoan Yang, G.M.A.M. El-Fallah, Qing Tao, Jun Fu, Chenlei Leng, Jennifer H. Shepherd, Hongbiao Dong
Read original paper ↗ ·Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti–6Al–2Sn–4Zr–2Mo–Si Alloy Having Duplex α + α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning
MATERIALS TRANSACTIONS · 2022-12-24
Irvin Séchepée, Paul Paulain, Yuka Nagasaki, Riku Tanaka, Hiroaki Matsumoto, Vincent Velay
Read original paper ↗ ·Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions
IEEE Communications Surveys & Tutorials · 2023-01-01
Nour Moustafa, Nickolaos Koroniotis, Marwa Keshk, Albert Y. Zomaya, Zahir Tari
Read original paper ↗ ·Impact of Data Sampling on Performance and Robustness of Machine Learning Models in Production Engineering
Lecture notes in production engineering · 2023-01-01
Felix Conrad, Eugen Boos, Mauritz Mälzer, Hajo Wiemer, Steffen Ihlenfeldt
Read original paper ↗ ·The effect of element characteristics on bainite transformation start temperature using a machine learning approach
Journal of Materials Science · 2023-01-01
Yangni Liu, Tingping Hou, Zhuang Yan, Tao Yu, Junwen Duan, Yuhui Xiao, Kaiming Wu
Read original paper ↗ ·Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN
steel research international · 2023-02-25
Lu Xie, Weigang Li, Lixia Fan, Mingxing Zhou
Read original paper ↗ ·Need for UAI–Anatomy of the Paradigm of Usable Artificial Intelligence for Domain-Specific AI Applicability
Multimodal Technologies and Interaction · 2023-02-28
Hajo Wiemer, Dorothea Schneider, Valentin Lang, Felix Conrad, Mauritz Mälzer, Eugen Boos, Kim Feldhoff, Lucas Drowatzky, Steffen Ihlenfeldt
Read original paper ↗ ·Machine learning study on time–temperature–transformation diagram of carbon and low-alloy steel
Journal of Iron and Steel Research International · 2023-03-22
Xiaoya Huang, Biao Zhang, Qiang Tian, Hong‐Hui Wu, Bin Gan, Zhongnan Bi, Weihua Xue, Asad Ullah, Hao Wang
Read original paper ↗ ·Material Stiffness Prediction Based on Neural Network and Symbolic Regression
International Conference on Computer Engineering and Application (ICCEA) · 2023-04-07
Jixuan Yi, Yiwen Li, Kai Zhang
Read original paper ↗ ·Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Matter · 2023-06-01
Xing Quan Wang, Pengguang Chen, Cheuk Lun Chow, Denvid Lau
Read original paper ↗ ·Estimation for Microstructure Formation Factor in TRIP-aided Multi-phase Steel by Using Primary Component Analysis of Data on the Elemental Distribution
鐵と鋼 : 日本鐡鋼協會々誌 · 2023-06-29
Koutarou Hayashi, Yuichi Kinoshita, Rina Fujimura, Manabu Takahashi
Read original paper ↗ ·Prediction of Mechanical Properties of Aluminium Alloy Strip Using the Extreme Learning Machine Model Optimized by the Gray Wolf Algorithm
Advances in Materials Science and Engineering · 2023-07-04
Zhenqiang Xiong, Jiadong Li, Peng Zhao, Yong Li
Read original paper ↗ ·Data-driven machine learning for alloy research: Recent applications and prospects
Materials Today Communications · 2023-07-19
Xueyun Gao, Haiyan Wang, Huijie Tan, Lei Xing, Zhiyu Hu
Read original paper ↗ ·Composition design of high-performance copper alloy by coupling artificial neural network and genetic algorithm
Computational Materials Science · 2023-08-21
Kang Liu, Ruijie Zhang, Si Zhang, Yongqin Chang, Mingyang Li, Yijie Wang, Qingqing Liu, Daohan Yu
Read original paper ↗ ·Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology
Materials Genome Engineering Advances · 2023-09-01
Xiaoxiao Geng, Feiyang Wang, Hong‐Hui Wu, Shuize Wang, Guilin Wu, Junheng Gao, Haitao Zhao, Chaolei Zhang, Xinping Mao
Read original paper ↗ ·Modeling Yield Strength of Austenitic Stainless Steel Welds Using Multiple Regression Analysis and Machine Learning
Metals · 2023-09-20
Sukil Park, Myeonghwan Choi, Dong-Yoon Kim, Cheolhee Kim, Namhyun Kang
Read original paper ↗ ·An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using a Feature Engineering Technique in Machine Learning
Metals · 2023-09-25
Tiehan Fan, Jianxin Hou, Jian Hu
Read original paper ↗ ·Application of machine learning methods for the prediction of roll force and torque during plate rolling of micro-alloyed steel
Journal of Alloys and Metallurgical Systems · 2023-10-20
Suman Kant Thakur, Alok Kumar Das, Bimal Kumar Jha
Read original paper ↗ ·A Systematic Review of Medium‐Mn Steels with an Assessment of Fatigue Behavior
steel research international · 2023-11-07
Deepak Kumar, Indrani Sen, Tapas Kumar Bandyopadhyay
Read original paper ↗ ·Data driven performance prediction of titanium-based matrix composites
Alexandria Engineering Journal · 2023-11-22
Xiaoling Wu, Yunfeng Zhou, Jinxian Zhang, Jiahao Liang
Read original paper ↗ ·Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning
Materials · 2023-11-26
Chengcheng Liu, Xuandong Wang, Weidong Cai, Jiahui Yang, Hang Su
Read original paper ↗ ·Artificial Intelligence and Machine Learning In Metallurgy. Part 2. Application Examples
Metallurgist · 2024-01-01
P. Yu. Zhikharev, Alexander Vadimovich Muntin, D. A. Brayko, M.O. Kryuchkova
Read original paper ↗ ·A dual-population multiobjective co-evolutionary matching ensemble learning for product multi-indicator prediction in continuous annealing
Neurocomputing · 2024-01-05
Yao Wang, Xianpeng Wang
Read original paper ↗ ·Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction
Swarm and Evolutionary Computation · 2024-03-04
Yao Wang, Xianpeng Wang, Xianpeng Wang, Lixin Tang
Read original paper ↗ ·Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning
Materials Today Communications · 2024-04-05
Zhiyang Qin, Hongliang Zhao, Shuya Zhang, Yuheng Fan, Xianglei Dong, Zishuo Lan, Xiaobing Hu, Yang Song, Chunwen Guo
Read original paper ↗ ·Multiobjective Evolutionary Learning for Multitask Quality Prediction Problems in Continuous Annealing Process
IEEE Transactions on Neural Networks and Learning Systems · 2024-04-23
Chang Liu, Lixin Tang, Kainan Zhang, Xuanqi Xu
Read original paper ↗ ·Artificial Neural Network-Based Modelling for Yield Strength Prediction of Austenitic Stainless-Steel Welds
Applied Sciences · 2024-05-16
Sukil Park, Cheolhee Kim, Namhyun Kang
Read original paper ↗ ·Symbolic regression as a feature engineering method for machine and deep learning regression tasks
Machine Learning Science and Technology · 2024-05-28
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Read original paper ↗ ·Machine Learning Combining High-Temperature Experiments for the Prediction of Wetting Angle of Mold Fluxes
Metallurgical and Materials Transactions B · 2024-07-01
Zichao Wang, Kun Dou, Wanlin Wang, Haihui Zhang, Jie Zeng
Read original paper ↗ ·Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning
Materials Today Communications · 2024-07-29
Xiaojun Wang, Xu Li, Hao Yuan, Na Zhou, Haishen Wang, Wenjian Zhang, Yafeng Ji
Read original paper ↗ ·Development of a model for iron concentrate tonnage with least angle regressions – An industrial trial
Canadian Metallurgical Quarterly · 2024-07-31
Mohsen Izadi-Yazdan Abadi, Reza Shokrizadeh, Farzane Heydari
Read original paper ↗ ·Sealing rubber ring design based on machine learning algorithm combined progressive optimization method
Tribology International · 2024-08-31
Weiru Zhou, Zonghong Xie
Read original paper ↗ ·Application of artificial intelligence to enhance manufacturing quality and zero-defect using CRISP-DM framework
International Journal of Production Research · 2024-09-28
Mehret Getachew, Birhanu Beshah, Ameha Mulugeta, Daniel Kitaw
Read original paper ↗ ·Empowering the Sustainable Development of High‐End Alloys via Interpretive Machine Learning
Advanced Materials · 2024-10-14
Hongtao Zhang, Huadong Fu, Weidong Li, Lei Jiang, Wei Yong, Jingtai Sun, Long‐Qing Chen, Jianxin Xie
Read original paper ↗ ·Prediction model of mechanical properties of hot-rolled strip based on improved feature selection method
Journal of Iron and Steel Research International · 2024-10-14
Zhiwei Gao, Guangming Cao, Siwei Wu, Deng Luo, Hou-xin Wang, Zhenyu Liu
Read original paper ↗ ·Application of Combined Machine Learning Methods in Metallurgy FOR Analysis, Selection of Materials and Prediction of Properties
International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · 2024-11-13
Vladislav Yurov, Sergei Kuzenkov, Игорь Анатольевич Цыганов
Read original paper ↗ ·Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti-6Al-2Sn-4Zr-2Mo-Si Alloy Having Duplex α+α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning
Journal of the Japan Institute of Metals and Materials · 2024-11-24
Irvin Séchepée, Paul Paulain, Yuka Nagasaki, Riku Tanaka, Hiroaki Matsumoto, Vincent Velay
Read original paper ↗ ·Machine and deep learning performance in out-of-distribution regressions
Machine Learning Science and Technology · 2024-12-01
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Read original paper ↗ ·Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling
Journal of Molecular Modeling · 2024-12-12
Gerardo M. Casañola‐Martín, Jing Wang, Jian‐Ge Zhou, Bakhtiyor Rasulev, Jerzy Leszczyński
Read original paper ↗ ·A hybrid knowledge-guided and data-driven method for predicting low-alloy steels performance
Computational Materials Science · 2024-12-27
Conghua Qi, Chengmeng Wang, Dongmei Fu, Lizhen Shao, Ke Zhou, Zhiyi Zhao
Read original paper ↗ ·Machine learning accelerate the design of low cost and high strength Cu-Zn alloys based on the small sample dataset
SSRN Electronic Journal · 2025-01-01
Jian Chen, Fei Zhou, Shumin Han, Xin Chen, Mingqing Liao, Hongli Wang, Yong Cao
Read original paper ↗ ·Machine learning accelerate the design of low cost and high strength Cu-Zn alloys based on the small sample dataset
SSRN Electronic Journal · 2025-01-01
Jian Chen, Fei Zhou, Shumin Han, X. W. Chen, Mingqing Liao, Hongli Wang, Yong Cao
Read original paper ↗ ·Quantifying the effects of cooling rates on Fe-rich intermetallics in recycled Al-Si-Cu alloys by machine learning
Journal of Alloys and Compounds · 2025-01-17
Quan Li, Junsheng Wang, Chengpeng Xue, Yisheng Miao, Qinghuai Hou, Yanan Meng, Xinghai Yang, Xingxing Li
Read original paper ↗ ·