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RESEARCH PAPER · Journal of Manufacturing and Materials Processing · 2023

Machine-Learning-Based Thermal Conductivity Prediction for Additively Manufactured Alloys

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Machine-Learning-Based Thermal Conductivity Prediction for Additively Manufactured AlloysOriginalAn Investigation Into the Challenges of Contemporary Additive Manufacturing: Insights Into the Metallurgical Response of Materials and Relevant Solution0Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression30Laser Powder Bed Fusion: Exploring Recent Technological Breakthroughs and Alloy Utilization1Chemical short-range order increases the phonon heat conductivity in a refractory high-entropy alloy27Designing the Chemical Composition of Steel with Required Hardenability Using Computational Methods5Machine Learning-Driven Prediction of Thermal Conductivity in Fe Alloys1Additive manufactured enabled digital metallurgy processes, challenges and future prospects13A Systematic and General Machine Learning Approach to Build a Consistent Data Set from Different Experiments: Application to the Thermal Conductivity of Methane4Predicting Thermal Resistance of Packaging Design by Machine Learning Models13Integrating machine learning and DFT for hardness prediction in high-entropy alloys6Theoretical modelling for accurate prediction of transient rapid temperature rise variation within whole tool tip area0An image-based deep learning framework for predicting thermal conductivity of polymer composites with rotational data augmentation3Analysis of thermal conductivity of aluminum alloys by compositions and tempering process using machine learning11The emerging role of machine learning in nanomaterials research: applications, challenges, and future directions8Opportunities and Challenges of Technical Ceramics for Thermal Management in Aviation0Machine learning in conductivity development of specific alloys1Machine Learning for Alloy Design: A Property-Oriented Review3Bayesian-optimized deep neural network surrogate for orientation-driven anisotropic thermal conductivity prediction in hybrid polymer nanocomposites5Thermal Prediction for Efficient Management of Temperatures in System-in-Package (SiP) Using Machine Learning (ML)0The place and future of AI in thermal analysis processes: from prediction to foresight1Revealing the structure–property relationships of copper alloys with FAGC0Predicting thermal conductivity of transition metals using machine learning gaussian process regression models0

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  1. An Investigation Into the Challenges of Contemporary Additive Manufacturing: Insights Into the Metallurgical Response of Materials and Relevant Solution

    Unknown journal · 2023-10-30

    Huan Wen Ding

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  2. Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression

    Applied Sciences · 2024-03-18

    Wendimu Fanta Gemechu, Wojciech Sitek, Gilmar Ferreira Batalha

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  3. Laser Powder Bed Fusion: Exploring Recent Technological Breakthroughs and Alloy Utilization

    Preprints.org · 2024-05-31

    Warris Hedric

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  4. Chemical short-range order increases the phonon heat conductivity in a refractory high-entropy alloy

    Scientific Reports · 2024-09-04

    Geraudys Mora-Barzaga, Herbert M. Urbassek, Orlando R. Deluigi, P. M. Pasinetti, Eduardo M. Bringa

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  5. Designing the Chemical Composition of Steel with Required Hardenability Using Computational Methods

    Metals · 2024-09-19

    Neven Tomašić, Wojciech Sitek, Wendimu Fanta Gemechu, Dario Iljkić

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  6. Machine Learning-Driven Prediction of Thermal Conductivity in Fe Alloys

    Unknown journal · 2024-10-25

    Ganesh D Sathe, Jayesh Jadhav, Manik Kadam, Anagha M. Pathak, Sandesh Jadkar, Vishal Bhalla

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  7. Additive manufactured enabled digital metallurgy processes, challenges and future prospects

    Applied Materials Today · 2024-12-31

    Sohail Ali, Mayur Jiyalal Prajapati, Chinmai Bhat, Cho‐Pei Jiang, Jeng‐Ywan Jeng

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  8. A Systematic and General Machine Learning Approach to Build a Consistent Data Set from Different Experiments: Application to the Thermal Conductivity of Methane

    ACS Engineering Au · 2025-03-11

    Matheus Máximo‐Canadas, Júlio César Duarte, Jakler Nichele, Leonardo Santos de Brito Alves, Luiz Octávio Vieira Pereira, Rogério Ramos, Itamar Borges

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  9. Predicting Thermal Resistance of Packaging Design by Machine Learning Models

    Micromachines · 2025-03-19

    Jung-Pin Lai, Shane Lin, Vito Lin, Andrew Kang, Yu-Po Wang, Ping‐Feng Pai

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  10. Integrating machine learning and DFT for hardness prediction in high-entropy alloys

    MRS Communications · 2025-05-22

    Shanker Kumar, Vikas Jindal

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  11. Theoretical modelling for accurate prediction of transient rapid temperature rise variation within whole tool tip area

    Applied Thermal Engineering · 2025-06-07

    Lirui Zheng, Jinfu Zhao, Zhanqiang Liu, Bing Wang, Xiaoping Ren, Pengyang Wang

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  12. An image-based deep learning framework for predicting thermal conductivity of polymer composites with rotational data augmentation

    Materials & Design · 2025-08-29

    Mingeon Kim, Geonhwi Lee, Seung-Kyum Choi, Hae-Jin Choi

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  13. Analysis of thermal conductivity of aluminum alloys by compositions and tempering process using machine learning

    Scientific Reports · 2025-09-29

    Adnan Roshid Shawon, Rittika Ghosh, Md Ashraful Islam

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  14. The emerging role of machine learning in nanomaterials research: applications, challenges, and future directions

    Journal of Nanoparticle Research · 2025-10-25

    Juhi Jannat Mim, Saifuddun Rakib, Shahana Akter, Jannat Rosul Nisha, Safiullah Khan, Sayma Rahman, Mehedi Hasan Manik, Nayem Hossain

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  15. Opportunities and Challenges of Technical Ceramics for Thermal Management in Aviation

    Lecture notes in mechanical engineering · 2026-01-01

    Lorenzo Mazzei, Niccolò Casini, Sérgio Machado, Riccardo Da Soghe, Lisa Mikiss, Martin Schwentenwein

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  16. Machine learning in conductivity development of specific alloys

    Canadian Metallurgical Quarterly · 2026-02-27

    Omid Karimi Sadaghiani, Sahba Tabarraei, İlknur Tunç, Wei Peng, Raha Shabani

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  17. Machine Learning for Alloy Design: A Property-Oriented Review

    Alloys · 2026-03-06

    Shamim Pourrahimi, Soroosh Hakimian

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  18. Bayesian-optimized deep neural network surrogate for orientation-driven anisotropic thermal conductivity prediction in hybrid polymer nanocomposites

    Results in Engineering · 2026-03-12

    Lakshmaiya Natrayan, Naveen Kilari, Naga Dheeraj Kumar Reddy Chukka, Seeniappan Kaliappan, Anand Rajendran, Ramya Maranan

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  19. Thermal Prediction for Efficient Management of Temperatures in System-in-Package (SiP) Using Machine Learning (ML)

    Applied Sciences · 2026-05-31

    Aziz Oukaira, Mhamed Filali Baba, Ouafaa Ettahri, Ahmed Lakhssassi

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  20. The place and future of AI in thermal analysis processes: from prediction to foresight

    Neural Computing and Applications · 2026-06-01

    Emre Arpaci

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  21. Revealing the structure–property relationships of copper alloys with FAGC

    Physica B Condensed Matter · 2026-08-07

    Yuexing Han, RuiJie Li, Guanxin Wan, Gan Hu, Yi Liu, Bing Wang

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  22. Predicting thermal conductivity of transition metals using machine learning gaussian process regression models

    Transition Metal Chemistry · 2026-09-02

    Kapil Dev Mahato

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