Microstructure and dislocation structure evolution during creep life of Ni-based single crystal superalloys

The high performance of Ni single crystal superalloys during high temperature low stress creep service,is intrinsically determined by the combined effects of microstructural evolution and the dislocation behaviour.In the field of the evolution of dislocation network,two main recovery mechanism based on dislocation migration dominate the process.One is superdislocations shearing intoγ’rafts through a two-superpartials-assisted approach.Another is the compact dislocations migrating alongγ/γ’interface.These two mechanisms are similarly climb-rate-controlled process.In this work,a model for the minimum creep rate based on thermodynamic and kinetic calculations and using an existing detailed dislocation dynamics model has been built by taking the dislocation migration behaviours as well as the rafted microstructure into consideration,which can well reproduce the([100]tensile)creep properties of existing Ni superalloy grades,without the need to make the dislocation parameter values composition dependent.

A Review of Self-healing Metals: Fundamentals, Design Principles and Performance

Self-healing metals possess the capability to autonomously repair structural damage during service. While self-healing concepts remain challenging to be realized in metals and metallic systems due to the small atomic volume of the mobile atoms, the slow diffusion unless at high temperatures and the strong isotropic metallic bonds, the scientific interest has increased sharply and promising progress is obtained. This article provides a comprehensive and updated review on the developments and limitations associated with the various modes of potentially healable damage induced in metals and alloys, i.e., stressinduced damage, irradiation-induced damage in bulk materials and contact damage in corrosion protective coatings. The spontaneous intrinsic healing mechanisms not requiring external assistance other than the material operating at the right temperature and an assisted healing mechanism with external intervention are reviewed. Promising strategies to achieve self-healing in metals are identified. Finally, we give some prospects for future research directions in self-healing metals.

Predicting the Transition between Upper and Lower Bainite via a Gibbs Energy Balance Approach

The transition temperature between upper bainite and lower bainite is calculated with an extended Gibbs energy balance model, which is able to quantitatively describe the evolution of carbon supersaturation within bainitic ferrite sheaves during the entire thickening process. The nucleation rate of intra-lath cementite precipitation on a dislocation is calculated based on of the degree of carbon supersaturation.Upper bainite and lower bainite are thus distinguished by the effective nucleation density and therefore a numerical criterion can be set to define the transition. The model is applied to Fe-xC-1Mn/2Mn/1 Mo ternary alloys. Results show that the transition temperature increases with bulk carbon content at lower carbon concentration but decreases in the higher carbon region. This prediction agrees very well with the experimental observations in Mn and Mo alloyed systems. Moreover, the highest transition temperature and the carbon content at which it occurs in the Fe-xC-2Mn system are in good agreement with reported experimental data. The inverse ＂V＂ shaped character of the carbon concentration-transition temperature curve indicates two opposite physical mechanisms operating at the same time. An analysis is carried out to provide an explanation.

Charting the ‘composition–strength’ space for novel austenitic,martensitic and ferritic creep resistant steels

We report results of a large computational ＇alloy by design＇ study, in which the ＇chemical composition-mechanical strength＇ space is explored for austenitic, ferritic and martensitic creep resistant steels. The approach used allows simultaneously optimization of alloy composition and processing parameters based on the integration of thermodynamic, thermo-kinetics and a genetic algorithm optimization route. The nature of the optimisation depends on both the intended matrix（ferritic, martensitic or austenitic） and the desired precipitation family. The models are validated by analysing reported strengths of existing steels. All newly designed alloys are predicted to outperform existing high end reference grades.

A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning

We present an electron backscattered diffraction(EBSD)-trained deep learning(DL)method integrating traditional material characterization informatics and artificial intelligence for a more accurate classification and quantification of complex microstructures using only regular scanning electron microscope(SEM)images.In this method,EBSD analysis is applied to produce accurate ground truth data for guiding the DL model training.An U-Net architecture is used to establish the correlation between SEM input images and EBSD ground truth data using only small experimental datasets.The proposed method is successfully applied to two engineering steels with complex microstructures,i.e.,a dual-phase(DP)steel and a quenching and partitioning(Q&P)steel,to segment different phases and quantify phase content and grain size.Alternatively,once properly trained the method can also produce quasi-EBSD maps by inputting regular SEM images.The good generality of the trained models is demonstrated by using DP and Q&P steels not associated with the model training.Finally,the method is applied to SEM images with various states,i.e.,different imaging modes,image qualities and magnifications,demonstrating its good robustness and strong application ability.Furthermore,the visualization of feature maps during the segmenting process is utilised to explain the mechanism of this method’s good performance.