Effect of Si and C additions on the reaction mechanism and mechanical properties of FeCrNiCu high entropy alloy

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively

Influence of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys

© 2020 Chinese Materials Research Society The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively

Effect of Si and C additions on the reaction mechanism and mechanical properties of FeCrNiCu high entropy alloy

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively

Influence of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys

© 2020 Chinese Materials Research Society The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively

Learning non-Markovian Decision-Making from State-only Sequences

Conventional imitation learning assumes access to the actions of demonstrators, but these motor signals are often non-observable in naturalistic settings. Additionally, sequential decision-making behaviors in these settings can deviate from the assumptions of a standard Markov Decision Process (MDP). To address these challenges, we explore deep generative modeling of state-only sequences with non-Markov Decision Process (nMDP), where the policy is an energy-based prior in the latent space of the state transition generator. We develop maximum likelihood estimation to achieve model-based imitation, which involves short-run MCMC sampling from the prior and importance sampling for the posterior. The learned model enables \textit{decision-making as inference}: model-free policy execution is equivalent to prior sampling, model-based planning is posterior sampling initialized from the policy. We demonstrate the efficacy of the proposed method in a prototypical path planning task with non-Markovian constraints and show that the learned model exhibits strong performances in challenging domains from the MuJoCo suite

Graph-guided Architecture Search for Real-time Semantic Segmentation

Designing a lightweight semantic segmentation network often requires researchers to find a trade-off between performance and speed, which is always empirical due to the limited interpretability of neural networks. In order to release researchers from these tedious mechanical trials, we propose a Graph-guided Architecture Search (GAS) pipeline to automatically search real-time semantic segmentation networks. Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint. Specifically, to produce the cell-level diversity, the cell-sharing constraint is eliminated through the cell-independent manner. Then a graph convolution network (GCN) is seamlessly integrated as a communication mechanism between cells. Finally, a latency-oriented constraint is endowed into the search process to balance the speed and performance. Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS achieves the new state-of-the-art trade-off between accuracy and speed. In particular, on Cityscapes dataset, GAS achieves the new best performance of 73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202