A study of bionics micro-textures on the surface of HA bio-coatings prepared by nanosecond laser

The material and surface morphology of bone implants can influence cellular activity and proliferation properties. The preparation of biomimetic microtextures on the hydroxyapatite (HA) bio-coated surface can enhance the biological properties of the implant. In this study, a nanosecond laser was used to prepare microtextures on the surface of HA coatings, and the effect of laser parameters on the ablation morphology was investigated by micromorphological observation and response surface methods. The ablation morphology was found to be better when the laser repetition frequency was 20 kHZ, the scanning speed was 300 mm/s and the number of scans was 7. Microtextures imitating fish scales and crocodile skin of different sizes were prepared on the surface of HA coatings using this laser parameter. The biological properties of the microtextures were investigated using CCK-8 tests and cell adhesion tests and the nanosecond laser-prepared mimetic microtextures were found to promote cell proliferation. A 0.1 mm radius of the mock fish scale microtexture had optimal cellular activity, where the tentacles of the cells could fully extend to the surface of the microstructure, which facilitated cell adhesion and proliferation.     

轮腿式机器人轮-腿高效移动策略优化

复杂地形下的高效移动策略是轮腿式机器人研制过程中的技术难点。本文在常规移动策略的基础上，通过引入关节空间状态量描述支腿相对于机身的位姿，引入位姿转换量描述相邻时序的位姿状态量间的运动过程，进而建立起移动策略与时间和能耗的数学模型，并以移动时间最短和能耗最低为目标，建立了移动策略的优化模型，通过优化迭代形成轮-腿高效移动策略。复杂地形下的越障仿真表明，机器人采用轮-腿高效移动策略可实现越障功能，与常规移动策略相比，移动时间及能耗均明显降低，验证了轮-腿高效移动策略的有效性。     

Classification of Tool Wear State based on Dual Attention Mechanism Network

To assure the quality of product processing, precise abrasion detections must be performed on the machine's cutting tools. Consequently, the improvement of abrasion detection is crucial for the upkeep of devices in terms of processing capacity and cutting performance. The technique of tool surface abrasion imaging is one of the detection methods. This paper proposes a deep learning and computer vision-based monitoring model for conducting abrasion monitoring over cutting tools, as conventional imaging techniques always require high precision and are criticized for a complicated calculation process and their time-consuming nature resulting from manual calibration. This method is built on the SE-ResNet50 based online abrasion state monitoring model and introduces an enhanced dual-attention mechanism to learn the dependency of pixel characteristics and the inter-correlation between channels, respectively. It is proposed that the Enhance Module Network capture the underlying information on a greater scale. To achieve the self-adaptive perception of the network weights corresponding with distinct abrasion categories, attributes are recovered from the input photos, hence eliminating the complexity and restrictions associated with manual extraction. The established abrasion status categorization method is experimentally validated on a three-axis milling machine with cemented carbide tools. The results indicated that the proposed method can classify tool wear state more accurately from the raw data collected by industrial cameras under the premise of ensuring efficiency. Its recognition accuracy is up to 96.99%, and the generalization ability can obtain good results, which provides a novel concept for tool condition monitoring in actual industrial scene.     

Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion

Improper fuel loading decision results in carrying excessive dead weight during flight operation, which will burden the airline operation cost and cause extra waste emission. Existing works mainly focused on the post-event fuel consumption based on flight trajectory. In this work, a novel deep learning model, called FCPNet, is proposed to achieve the fuel consumption prediction (FCP) before the flight departure. Considering the influential factors for aircraft performance, the multi-modal information sources, including the planned route, weather information, and operation details, are selected as the model input to predict fuel consumption. Correspondingly, three modules are innovatively proposed to learn embedding features from multi-modal inputs. Based on the planned route, the graph convolutional network is proposed to mine the spatial correlations in the non-Eulerian route network. Considering the grid attributes of the weather information, the ConvLSTM is applied to learn abstract representations from both the temporal and spatial dimensions, in which the three-dimensional convolution neural networks are also designed to fine-tune intermediate feature maps. The fully connected layer is also proposed to learn informative features from operation details. Finally, an attention-based fusion network is presented to generate the final embedding by considering the unique contributions of the multi-modality sources, which are further applied to predict flight fuel consumption. A binary encoding representation is proposed to formulate the FCP task as a multi-binary classification problem. The proposed model is validated on a real-world dataset, and the results demonstrate that it outperforms other baselines, i.e., achieving a 6.50% mean absolute percentage error, which can practically support the airline operation and global emission control before flight departure.     

Effects of ultrasonic vibration assisted milling with laser ablation pretreatment on fatigue performance and machining efficiency of SiCf/SiC composites

Due to the poor machinability of SiC_f/SiC parts in machining, many problems are caused, such as low machining efficiency, poor machining quality and high processing cost, which seriously limit its manufacturing and application. A novel process ultrasonic vibration assisted milling with laser ablation pretreatment (UVAMLAP) was proposed to optimize the fatigue performance and machining efficiency of SiC_f/SiC parts. This process was used to fabricate specimens, which were then tested for tensile strength and fatigue performance. The results show that UVAMLAP could enhance surface quality, and increase the tensile strength and residual tensile strength of the sample by 9.4% and 13.5%. This process can avoid damage aggravation in the initial stage of failure, weaken matrix fracture and interface debonding velocity, and reduce fatigue performance degradation caused by machining damage. In addition, comprehensive evaluation based on multi-dimensional indicators such as milling quality, machining efficiency and tool cost for machining strategy was carried out by taking tensile sample machining as an example. The UVAMLAP process can not only improve the machined surface quality, but also reduce the machining time by 31.3% and the tool cost by 75%. Therefore, UVAMLAP provides a feasible process scheme for high-efficiency and low-damage machining of SiC_f/SiC parts.     

Nitride phosphor-in-glass films enabling high-performance white light in transmissive and reflective laser lighting

Phosphor-converted laser lighting has become a credible candidate in next-generation high-brightness white lighting, and the configuration types of phosphor converters have a great influence on the opto-thermal performances of laser lighting. In this work, we proposed nitride phosphor-in-glass films (PIGFs) for high-brightness laser lighting and investigated the opto-thermal performances of PIGFs in transmissive (T) and reflective (R) modes. The Y-PIGFs were prepared by low-temperature sintering a mixture of yellow-emitting La₃Si₆N₁₁:Ce³⁺ (LSN) phosphor and borosilicate glass, and the Y/R-PIGFs were achieved by incorporating red-emitting CaAlSiN₃:Eu²⁺ (CASN) phosphor into the Y-PIGFs. The PIGFs display higher thermal stability and luminescence intensity than the raw phosphors. By tailoring the thickness of Y-PIGFs, the Y-PIGF with a film thickness of 75 μm achieves the luminous efficacy of 199.4 lm/W and 91.5 lm/W in the T mode and R mode, respectively, and the PIGF realizes the highest luminous efficacy of 207.8 lm/W by collecting backward light in the T mode. At the CASN/LSN ratio of 0.20, the Y/R-PIGF enables high-quality white light with a color rendering index (CRI) higher than 89. Furthermore, under 4.82 W laser excitation, the central temperatures of Y-PIGF in the T-mode and R-mode are only 98 °C and 67.4 °C, respectively. The results indicate that the PIGFs enable high-performance white laser lighting with distinct opto-thermal properties by adjusting configuration types.     

Structural,dielectric, electric and magnetic properties of magnesium substituted lithium nanoferrites

Magnesium substituted Li-ferrites nanoparticles with chemical composition Li_0.5-0.5xMg_xFe_2.5-0.5xO₄ (x = 0.0, 0.2, 0.4, 0.6, 0.8, and 1) synthesized by sol-gel auto combustion method. Synthesized materials were sintered at 600 °C for 4 h in the air and heated samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive x-ray spectroscopy (EDS) techniques. Rietveld refinement of XRD patterns confirmed the formation of the spinel structure where the lattice constant varied with the increase of Mg content. The crystallite size of magnesium-substituted lithium ferrites was calculated using Scherrer's equation and showed slight changes while the W-H plot shows more changes. The electrical conductivity and activation energy of Mg-substituted lithium ferrite were strongly affected by Mg content and by which they appear to confirm to semiconductor nature. The paramagnetic transition to ferrite was confirmed by the change in the Arrhenius plot, which showed a large variation between two regions that differ in the values of the activation energy, where the values of activation energies in the ferrite region were greater than those that appeared in the para region, as well as the variation between the activation energy values in the non-substituted lithium ferrite. The dielectric parameters such as the real part of the dielectric constant and loss tangent of the samples were analyzed in the range of 50 Hz to 5 MHz at room temperature, 100, 300, and 500 °C. Curie's temperature showed a decrease with the increase in Mg content. Hysteresis loops were measured using a vibrating sample magnetometer (VSM) where both the temperature and the applied magnetic field were changed. The FC result of Li_0.5-0.5xMg_xFe_2.5-0.5xO₄ (x = 0.2, 0.4, and 0.6) is almost flat below T_B which demonstrates that the Li–Mg nano ferrites show a super-spin glass behaviour.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.     

Effect of lattice distortion in high-entropy RE2Si2O7 and RE2SiO5 (RE=Ho,Er, Y,Yb, and Sc) on their thermal conductivity: Experimental and molecular dynamic simulation study

High-entropy materials are considered to be born with lattice distortion, which is still lack of comprehensive investigation in rare earth silicates to date. In this paper, we confirmed the existence of lattice distortions in high-entropy rare-earth silicates via experiments and molecular dynamic simulations. The effects of the lattice distortion on the thermophysical properties are elucidated. Simulation results indicate the lattice distortion is present in both cation and anion sublattice, leading to the compressing and stretching of atomic bond as well as fluctuation of atomic bond strength. Accordingly, lattice distortion in the Si and O sublattice is also verified by the Raman spectra. Furthermore, the thermal conductivity of high-entropy rare earth silicates remarkably reduces and exhibits glass-like behavior, which is confirmed by experiments in cooperation with molecular dynamic simulations. Simulation also reveals that the lifetimes and group velocities of vibrational modes are significantly reduced by lattice distortion, which result in the reduction of overall thermal conductivity of high-entropy rare-earth silicates.     

Investigation of the influence for ZnSe phase in Ag2ZnSnSe4 and ZnO/Ag2ZnSnSe4 photoanodes on their photoelectrochemical activities in salt water solution

In this study, quaternary Ag₂ZnSnSe₄ (AZTSe) photoanodes with low and high ratios of ZnSe phase are made to understand the influence on the charge-transfer mechanisms, reaction kinetics and photoelectrochemical activities in electrolyte. Photoelectrochemical activities of 7.5 and 5.25 mA/cm² at the given voltage of 1 V (vs. Ag/AgCl) are obtained using the AZTSe sample containing low ratio (atomic percentage of around 21% in sample) and high ratio (atomic percentage of around 37% in sample) of ZnSe phase, respectively. From the electrochemical measurements and X-ray absorption spectra of samples, the ZnSe phase can attract the light-driven electrons from the sample with low ratio of ZnSe phase (atomic percentage of 21% in sample). However, for the sample containing high ratio of ZnSe phase (atomic percentage of 37% in sample), the ZnSe acts as the recombination center and results in poor photoelectrochemical activity. With the modification of ZnO rods on sample, its long-term photoelectrochemical activity is improved due to high charge transportation kinetic and photo-driven holes accumulation on ZnO rods rather than on AZTSe sample. Our study reports a significant observation on the influence of ZnSe phase for photoelectrochemical salt-water splitting.