A Multiphysics Model for Predicting Microstructure Changes and Microhardness of Machined AerMet100 Steel

Zhang, Wenqian; Chen, Xupeng; Yang, Chongwen; Wang, Xuelin; Zhang, Yansong; Li, Yongchun; Xue, Huan; Zhou, Zheng

doi:10.3390/ma15134395

Cited by 3 publications

(1 citation statement)

References 34 publications

(52 reference statements)

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“…The advantage of the MPT over the other SPD methods (hard turning, vibrational balls hardening, an ultrasonic impact treatment, etc.) [14][15][16][17][18][19][20][21][22][23][24][25] consists of the formation of surface NCSs with high depth (up to 300 µm) and high microhardness values [11]. Moreover, alloying during the MPT plays an essential role in the stabilization of the NCS.…”

Section: Introductionmentioning

confidence: 99%

The Mode Deformation Effect on Surface Nanocrystalline Structure Formation and Wear Resistance of Steel 41Cr4

et al. 2023

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A surface nanocrystalline steel layer in the low alloy steel 41Cr4 was fabricated by using mechanical-pulse treatment (MPT) with different deformation modes. The structure parameters, the physical and mechanical properties, the wear resistance, and the surface topography parameters of the treated steel depending on the deformation mode were investigated. A tool with a smooth working surface was used for inducing unidirectional deformation in the top surface layer (shear), and a tool with the oppositely directed grooves was used for generating multidirectional deformation. The surface layer with a nanocrystalline structure formed by MPT using both of the tools was characterised by enhanced mechanical properties and wear resistance compared with those of the untreated or heat-treated steels. Inducing multidirectional deformation during the MPT resulted in a decrease in the grain size and an increase in the depth and microhardness of the surface layer due to it facilitating the generation of dislocations compared to those formed under unidirectional deformation. The results also demonstrated that favourable surface topography parameters providing the highest wear resistance of the steel were obtained at MPT using multidirectional deformation.

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Section: Introductionmentioning

confidence: 99%

The Mode Deformation Effect on Surface Nanocrystalline Structure Formation and Wear Resistance of Steel 41Cr4

et al. 2023

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Correlation between machining-induced surface alterations and stress corrosion cracking susceptibility of au stenitic stainless steels

Zhang,

Li,

Dong

et al. 2023

Journal of Materials Research and Technology

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Surface Mechanical Property Prediction and Process Optimization of 18CrNiMo7-6 Carburized Steel Stator Guide Based on Radial Basis Function Neural Network and NSGA-II Algorithm

Li,

Tang,

Chen

et al. 2024

Coatings

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The carburizing process is a key technology that affects the mechanical properties of the surface of the hydraulic motor stator guide rail, and the related process parameters have an important influence on surface hardness, the thickness of the carburized layer, and the deformation of the guide rail. However, at present, the relationship between the carburizing process parameters and the surface mechanical properties of the target is not clear. This paper proposes a “hardness prediction and process parameter optimization” method. Firstly, a finite element model is established, with carburizing time, temperature, and carbon potential as the three input factors; the optimal Latin hypercubic experimental design and sensitivity analysis are applied. Secondly, surface hardness, carburized layer thickness, and deformation are taken as the output values, and an RBF neural network is used to construct the prediction model. The results show that the RBF neural network can be accurately used for the prediction of surface hardness, the thickness of the carburized layer, and deformation, and for the optimization of process parameters. The optimized parameters of surface hardness and the thickness of the carburized layer were increased by 4.2% and 5.1%, respectively, and the deformation amount was reduced to 0.31 mm, achieving the goal of optimal design.

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A Multiphysics Model for Predicting Microstructure Changes and Microhardness of Machined AerMet100 Steel

Cited by 3 publications

References 34 publications

The Mode Deformation Effect on Surface Nanocrystalline Structure Formation and Wear Resistance of Steel 41Cr4

The Mode Deformation Effect on Surface Nanocrystalline Structure Formation and Wear Resistance of Steel 41Cr4

Correlation between machining-induced surface alterations and stress corrosion cracking susceptibility of au stenitic stainless steels

Surface Mechanical Property Prediction and Process Optimization of 18CrNiMo7-6 Carburized Steel Stator Guide Based on Radial Basis Function Neural Network and NSGA-II Algorithm

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