Enhancing Strength and Ductility of a Ni-26.6Co-18.4Cr-4.1Mo-2.3Al-0.3Ti-5.4Nb Alloy via Nanosized Precipitations, Stacking Faults, and Nanotwins.

The addition of Co to Ni-based alloys can reduce the stacking fault energy. In this study, a novel Ni-26.6Co-18.4Cr-4.1Mo-2.3Al-0.3Ti-5.4Nb alloy was developed by increasing the Co addition to 26.6 wt.%. A new strategy to break the trade-off between strength and ductility is proposed by introducing dense nanosized precipitations, stacking faults, and nanoscale twins in the as-prepared alloys. The typical characteristics of the deformed alloy include dense dislocations tangles, nanotwins, stacking faults, and Lomer-Cottrell locks. In addition to the pinning effect of the bulky δ precipitates to the grain boundaries, the nanosized γ' particles with a coherent interface with the matrix show significant precipitation strengthening. As a result, the alloy exhibits a superior combination of yield strength of 1093 MPa and ductility of 29%. At 700 °C, the alloy has a high yield strength of 833 MPa and an ultimate tensile strength of 1024 MPa, while retaining a ductility of 6.3%.

[Motion signal extraction method based on sEMG energy Gauss distribution characteristics].

Motion segment and extraction from continuous signals is the premise of surface electromyography (sEMG) analysis. For the problem that sEMG energy threshold required repeated manual testing to set, this the paper established a this mathematical model of continuous actions based on Gaussian sEMG energy curve, in which the energy threshold was set according to the distribution of Gaussian signal section, and differentiated the action signals from no-action signals combined with energy comparison method. The experiment results showed the method can achieve continuous repetitive action segmentation, and compared with manual segmentation of the same signal, has a higher degree of coincidence.

Study on sEMG-based exercise therapy for upper limb of severe hemiplegic patients.

sEMG, as a kind of bioelectrical signal reflecting muscle motion state, generally applies to motion recognition and human interface. Healthy subjects are selected in most studies, while for hemiplegic patients, especially patients with severe hemiplegia, high accuracy motion recognition is difficult to acquire due to the non-ideal sEMG signal from dysfunction muscles. Therefore, this paper presents an upper limb exercise therapy, based on 5 defined motions and 6 Muscle-Units, for patients with severe hemiplegia. Through the sampling and analysis of sEMG signals from 8 subjects, including 4 healthy and 4 hemiplegic patients, we draw a conclusion of the relevance between specific motions and Muscle-Units, which can be used as a reference for paralyzed arm training. According to this relevance, six Muscle-Units can be classified into two categories: major Muscle-Units and minor Muscle-Units. In order to improve the interest and positivity of patients, a PC based virtual interactive platform is established. The sEMG signal from major Muscle-Units is processed with a moving average algorithm, and the result is used as the control signal for training interaction.