Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset

Cui, Chunyuan; Cao, Zhuohan; Cao, Yang; Li, Jianjun; Dong, Zishuo; Wu, Shijing; Liu, Zhenyu

doi:10.1016/j.matdes.2022.111269

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…The integration of data‐driven methodologies and AI throughout the entire life cycle of steel materials can substantially enhance the efficiency of steel material R&D and foster engineering applications. In the steel industry, ML can not only carry out the R&D of high‐performance steel materials, but also it can be applied in the quality control of steel materials to realize the prediction and optimization of steel product quality [58,61,99] . By enabling real‐time monitoring and analysis, ML contributes to a reduction in defect rates, improving overall product quality.…”

Section: Discussionmentioning

confidence: 99%

“…The results demonstrate that the ML model combined with PM variables exhibits superior prediction performance compared to the model using the original dataset directly. Cui et al [58] . used physical metallurgical principles to convert high‐dimensional rolling process parameters into low‐dimensional microstructural features and integrated ML techniques to construct a deep neural network model.…”

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

“…Consequently, data mining using an industrial manufacturing dataset with high dimensions and small fluctuation of change presents a significant challenge [55] . To enhance the accuracy of modeling with industrial manufacturing datasets, three effective strategies have been developed: combining the ML model with multiscale calculation, [56] integrating the ML model with PM variables, [57,58] and expanding industrial data with literature data [59–61] …”

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

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Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Geng,

Wang,

et al. 2023

Materials Genome Engineering Advances

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With the development of new information technology, big data technology and artificial intelligence (AI) have accelerated material research and development and industrial manufacturing, which have become the key technology driving a new wave of global technological revolution and industrial transformation. This review introduces the data resources and databases related to steel materials. It then examines the fundamental strategies and applications of machine learning (ML) in the design and discovery of steel materials, including ML models based on experimental data, industrial manufacturing data, and simulation data, respectively. Given the advancements in big data, AI/ML, and new communication technologies, an intelligent manufacturing mode featuring digital twins is deemed critical in guiding the next industrial revolution. Consequently, the application of intelligence manufacturing with digital twins in the iron and steel industry is reviewed and discussed. Furthermore, the applications of ML in service performance prediction of steel products are addressed. Finally, the future development trends for data‐driven and AI approaches throughout the entire life cycle of steel materials are prospected. Overall, this work presents an in‐depth examination of the integration of data‐driven and AI technologies in the steel industry, highlighting their potential and future directions.

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Section: Discussionmentioning

confidence: 99%

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

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Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Geng,

Wang,

et al. 2023

Materials Genome Engineering Advances

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“…Berdasarkan persamaan tersebut dapat dilihatkan bahwa apabila suatu panjang gelombang sinar x yang dipakai diketahui dan sudut θhkl di ukur, diperkirakan dapat ditentukan Jarak d bidang difraksi dan jarak antar bidang difraksi, dhkl dapat ditentukan dengan struktur kubik serta memiliki hubungan terhadap struktur kristal pada parameter kisi dengan menggunakan persamaan 1 [26]. Setelah mengetahui kerapatan dislokasinya, kekuatan luluh (YS) dapat dihitung menggunakan persamaan 5 [27]. tempering perubahan yang telah terjadi dapat menunjukan bahwa nilai regangan kisi dan kerapatan dislokasi dapat meningkat sampai dua kali lipat [26].…”

Section: Pendahuluanunclassified

Pengaruh Waktu Tempering Terhadap Struktur Kristal, Kekerasan, dan Kuat Tarik Pada Baja VCN 150

Haris,

Budiarto

2023

JMEMME

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Baja VCN 150 termasuk dalam kategori baja ultra high-strength yang memiliki kekuatan sangat tinggi dan memadukan antara hardenability yang baik, keuletan, ketangguhan, dan ketahanan terhadap fatigue dan creep yang tinggi. Penulisan ini memaparkan pengaruh waktu tempering mengenai struktur kristal, kekerasan, serta kuat tarik pada baja vcn 150. Penelitian memakai metode penelitian kuantitatif yang menggunakan metode deskriptif dan studi eksperimental. Pengujian dilakukan melalui pengamatan struktur kristal memakai Difraktometer Sinar-X, uji kekerasan dan kuat tarik dengan Brinell. Hasil menunjukkan bahwa sifat mekanik dan fisik sangat dipengaruhi oleh proses quenching maupun tempering. Kekerasan material mengalami peningkatan setelah proses quenching dari 203 HB ke 393 HB menggunakan media quenching oli. Pada media pendingin air garam dapat meningkatkan kekerasan secara maksimal yaitu dari 203 HB ke 445,3 HB dan mengalami penurunan yang tidak signifikan kembali setelah melalui proses tempering. Penurunan kekerasan dari 445,3 HB ke 324 HB setelah 5 jam tempering. Penurunan ukuran kristal terjadi setelah proses tempering selama 5 jam yaitu dari 0,5766 (nm) ke 0,4725 (nm). Terjadi kenaikan nilai kerapatan diskolasi setelah proses tempering selama 5 jam yaitu 3,0679 (garis/mm²) ke 6,8527 (garis/mm²) begitu pula kenaikan regangan mikro setelah proses tempering selama 5 jam yaitu dari 0,1254 (ε) ke 0,2148 (ε).

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“…Gang et al 37 used a random forest to predict the hardness of boron steel based on 11 chemical composition and the distance along the Jominy bar to be able to construct the hardenability curve. Cui et al 38 presented a multi‐step process of predicting the yield strength of hot‐rolled Titan micro‐alloyed steels based on material composition, process parameters, and microstructural features using a deep ANN show better results than shallow ANN,

k

‐NN, SVM, and XGB.…”

Section: Overview On the Fatigue Of Pm Steels And Machine Learning Ap...mentioning

confidence: 99%

New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

Leininger

Reissner

Baumgartner

2022

Fatigue Fract Eng Mat Struct

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Up to now, no consistent fatigue assessment approach of powder metallurgy (PM) components is available. For some materials and for some parameters, such as the density, a relationship to the fatigue strength is known; however, for other materials, such relationships are unknown. Based on an extensive data set with 828 test series, the present work addresses this problem by conceiving and applying five machine learning (ML)-based approaches to increase the accuracy of the prediction of the fatigue life as well as to predict the scatter of unknown data as precisely as possible. With the elaborated procedure, on the one hand, a scatter range of T S ¼ 1 : 1:40 can be achieved on completely unknown data. On the other hand, by using a newly defined loss function, the standard deviation of unknown data can be predicted very accurately. The findings provide the basis for further research on cost and efficiency optimized design of PM components through better estimation of fatigue life.

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Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset

Cited by 11 publications

References 39 publications

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Pengaruh Waktu Tempering Terhadap Struktur Kristal, Kekerasan, dan Kuat Tarik Pada Baja VCN 150

New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

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