Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy

Du, Jinliang; Feng, Yun Li; Zhang, Minghe

doi:10.1016/j.jmrt.2021.10.111

Cited by 17 publications

(5 citation statements)

References 40 publications

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“…Lightweight is the main measure to achieve energy saving and emission reduction in the automotive industry in the future, and the use of advanced high-strength steel (AHSS) is an effective way to ensure vehicle safety performance and lightweight (Li et al, 2019;Du et al, 2021;Dong et al, 2022;Hong et al, 2022;Lai et al, 2022). AHSS is usually composed of two-phase or multi-phase microstructures with huge differences in mechanical properties (Sun et al, 2018;Feistle et al, 2022;Sedaghat-Nejad et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Impact of various heat treatment processes and welding speeds on the mechanical properties and microstructures of soft/hard composite joints

Yu,

Wang,

Chen

et al. 2024

Front. Mater.

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There is a certain contradiction between the formability and strength of auto parts. In this work, the whole-process processing technology of hot stamping soft steel was designed, and 500 MPa grade mild steel (500HS) with uniform microstructure was prepared. To take into account the strength and formability of hot stamping soft steel, here, based on laser welding technology, 500 MPa grade soft steel, and 1500 MPa grade hard steel are benignly composited, and by adjusting the laser welding speed and heat treatment process, the loss of mechanical properties caused by the weld seam is eliminated. A soft/hard composite steel for automobiles with excellent strength, ductility and formability is obtained. To maintain excellent deformation resistance and bonding force of the weld, the heat-affected zone of 500HS retains part of bainite and pearlite, which is beneficial to the strain compatibility and stress partitioning with the microstructure of the base metal, and the hardness is low. After heat treatment, which helps to transfer the stress concentration effect to the 500HS base metal with strong energy absorption capacity, so that the clad steel has excellent comprehensive mechanical properties. This process is developed based on existing industrialized equipment and has broad application prospects.

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Section: Introductionmentioning

confidence: 99%

Impact of various heat treatment processes and welding speeds on the mechanical properties and microstructures of soft/hard composite joints

Yu,

Wang,

Chen

et al. 2024

Front. Mater.

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“…Machine learning, a data-driven approach, has been employed to predict the properties of HEAs as well as several other alloys. Furthermore, material researchers have found it can overcome the limitations of the above-mentioned approaches [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] . Zhang et al 18 , Wen et al 27,30 , Zheng et al 29 , Klimenko et al 32 , Guo et al 23 , and Li et al 26 developed machine learning models which predict the mechanical properties of the HEAs.…”

Section: Introductionmentioning

confidence: 99%

“…Models have been reported for not only HEAs but also other alloys like steels, magnesium alloys, aluminum alloys, or copper alloys. Peng et al 17 , Lee et al 28 , Xie et al 31 , Shen et al 33,34 , Dey et al 19 , Suh et al 20 , Chen et al 21 , Du et al 22 , Desu et al 24 , and Zhang et al 25 have developed machine learning models to predict YS, ultimate tensile strength (UTS), total elongation (T.EL. ), or hardness.…”

Section: Introductionmentioning

confidence: 99%

A neural network model for high entropy alloy design

et al. 2023

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A neural network model is developed to search vast compositional space of high entropy alloys (HEAs). The model predicts the mechanical properties of HEAs better than several other models. It’s because the special structure of the model helps the model understand the characteristics of constituent elements of HEAs. In addition, thermodynamics descriptors were utilized as input to the model so that the model predicts better by understanding the thermodynamic properties of HEAs. A conditional random search, which is good at finding local optimal values, was selected as the inverse predictor and designed two HEAs using the model. We experimentally verified that the HEAs have the best combination of strength and ductility and this proves the validity of the model and alloy design method. The strengthening mechanism of the designed HEAs is further discussed based on microstructure and lattice distortion effect. The present alloy design approach, specialized in finding multiple local optima, could help researchers design an infinite number of new alloys with interesting properties.

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“…Machine learning (ML), a data-driven approach, has been employed to predict the mechanical properties, such as yield strength, hardness, elastic modulus and critical resolved shear stress of MCAs as well as several other alloys [ 33 , 34 , 35 , 36 ]. Furthermore, material researchers have found that this can overcome the limitations of the above-mentioned approaches [ 37 , 38 , 39 ]. The aforementioned studies successfully predicted properties using ML, but most of them did not design alloys or conduct experimental verifications of the designed alloys.…”

Section: Introductionmentioning

confidence: 99%

Accelerating First Principles Calculation of Multi-Component Alloy Steady-State Structure and Elastic Properties in Full Component Space

Yao,

Zhang,

Liu

et al. 2023

Materials

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The FeNiCrAlCoCuTi alloy system has great advantages in mechanical properties such as high hardness and toughness. It has high performance potential and research value and the key in research is designing alloy compositions with target properties. The traditional method, experimental analysis, is highly inefficient to properly exploit the intrinsic relationship between material characteristics and properties for multi-component alloys, especially in investigating the whole composition space. In this work, we present a research way that uses first principles calculation to obtain the properties of multi-component alloys and uses machine learning to accelerate the research. The FeNiCrAlCoCuTi alloy system with its elastic properties is used as an example to demonstrate this process. We specifically design models for each output, all of which have RMSE values of less than 1.1, and confirm their effectiveness through experimental data in the literature, showing that the relative error is below 5%. Additionally, we perform an interpretable analysis on the models, exposing the underlying relationship between input features and output. By means of spatial transformation, we achieve the prediction of the full-component spatial performance from binary to multiple components. Taking the FeNiCrAlM (M = Co, Cu, Ti) quinary alloy system as an example, we design a single-phase BCC structure composed of an Fe0.23Cr0.23Al0.23Ni0.03Cu0.28 alloy with a Young’s modulus of 273.10 GPa, as well as a single-phase BCC structure composed of an Fe0.01Cr0.01Al0.01Ni0.44Co0.53 alloy with a shear modulus of 103.6 GPa. Through this research way, we use machine learning to accelerate the calculation, which greatly shortens research time and costs. This work overcomes the drawbacks of traditional experiments and directly obtains element compositions and composition intervals with excellent performance.

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Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy

Cited by 17 publications

References 40 publications

Impact of various heat treatment processes and welding speeds on the mechanical properties and microstructures of soft/hard composite joints

Impact of various heat treatment processes and welding speeds on the mechanical properties and microstructures of soft/hard composite joints

A neural network model for high entropy alloy design

Accelerating First Principles Calculation of Multi-Component Alloy Steady-State Structure and Elastic Properties in Full Component Space

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