相关论文: Application-oriented strain-hardening engineering …
Exotic behaviour of mechanical metamaterials often relies on an internal transformation of the underlying microstructure triggered by its local instabilities, rearrangements, and rotations. Depending on the presence and magnitude of such a…
The High Nitrogen Martensitic Stainless Steel (HNMS) was subjected to three different austenitizing cycles of 1050$^\circ$C, 1075$^\circ$C and 1100$^\circ$C followed by subzero treatment at -70$^\circ$C. The fraction of retained austenite…
Titanium alloys are widely used in aerospace, biomedical, and energy applications owing to their high specific strength, corrosion resistance, and biocompatibility. Among them, $\alpha$-titanium alloys with a hexagonal close-packed (hcp)…
ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite…
This short paper presents the potential of using machine learning to predict materials behaviour in the context of hydrogen interaction with steel. Effort has been made to understand the quality, and amount of data needed to get improved…
Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation…
Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serves as a powerful tool for uncovering mathematical models…
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a…
Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when designing mechanical metamaterials with extreme performance.…
Self-shaping of curved structures, especially those involving flexible thin layers, has attracted increasing attention because of their broad potential applications in e.g. nanoelectromechanical/micro-electromechanical systems (NEMS/MEMS),…
From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous…
In the drive towards higher strength alloys, a diverse range of alloying elements is employed to enhance their strength and ductility. Limited solid solubility of these elements in steel leads to segregation during casting which affects the…
Twinning induced plasticity (TWIP) steels are high strength metallic materials with potential for structural components in e.g. automotive applications. However, they are prone to hydrogen embrittlement (HE) and galvanic corrosion. We…
Accurate predictions of thermo-mechanically coupled process in metals can lead to a reduction of cost and an increase of productivity in manufacturing processes such as forming. For modeling these coupled processes with the finite element…
We propose an approach for exploiting machine learning to approximate electronic fields in crystalline solids subjected to deformation. Strain engineering is emerging as a widely used method for tuning the properties of materials, and this…
Predicting the structural response of advanced multiphase alloys and understanding the underlying microscopic mechanisms that are responsible for it are two critically important roles modeling plays in alloy development. An alloys…
Local strain engineering is an exciting approach to tune the optoelectronic properties of materials. Two dimensional (2D) materials such as 2D transition metal dichalcogenides (TMDs) are particularly well suited for this purpose because…
Machine learning is becoming a powerful tool to predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use…
Nature provides examples of self-assemble lightweight disordered network structures with remarkable mechanical properties which are desirable for many applications purposes but challenging to reproduce artificially. Previous experimental…
About designing of mathematical model for forecasting of mechanical properties of hypopearlite steels both hot-rolled and strain-heat hardened state.