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The improvements of surface quality and dimensional accuracy are critical for Wire and Arc Additive Manufacturing (WAAM). This paper highlights a multi-objective optimization process for Bainite steel additive manufacturing. The welding design matrix for conducting the experiments was made by using the Box-Behnken design of response surface methodology (RSM). The input process parameters were varied at three levels which result in 46 experimental trials. The responses were measured during or after conducting the experiments. A second-order response surface model was developed and then multi-objective optimization was performed to obtain the desired surface appearance. The acceleration and staggered deposition processes were used to decrease the head dimension of single weld bead. The results show that the optimized sample surface appearance is smooth which has little spatters and no visible defects. Compared with the traditional processes which rely on overlapping rate adjustment but weaken the single weld bead morphology optimization, the process of this paper has comprehensive considerations of droplet transfer, heat input, and shaping coefficient. It enables the capacity of fabricating metal parts with high accuracy and lays a good foundation for Bainite steel additive manufacturing.

In order to investigate the impact of herbaceous root development on soil slope stability in expansive soil areas, the research was conducted in the soil slope experimental area of Yaoshi Town, Shangzhou District, Shangluo City. Three types of herbaceous plants, namely Lolium perenne, Medicago, and Cynodon dactylon, were planted to examine their influence on slope stability. The results indicated that Lolium perenne had significantly higher root length density and root surface area density compared to Cynodon dactylon and Medicago. However, the root weight density of Cynodon dactylon was found to be highest. The roots of Lolium perenne, Cynodon dactylon, and Medicago were predominantly observed in diameter ranges of 0 < L ≤ 1.0 mm, 0 < L ≤ 2.5 mm, and 2.5 < L ≤ 3.0 mm, respectively. The roots of herbaceous plants have the ability to enhance water retention in soil, resist hydraulic erosion of slope soil, and reduce soil shrinkage and swelling. During the initial phase of herbaceous planting, there is an accelerated process of organic carbon mineralization in the soil. The roots of herbaceous plants play a crucial role in soil consolidation and slope protection. They achieve this by dispersing large clastic particles, binding small particles together, altering soil porosity, enhancing soil water retention, and reducing soil water infiltration. It was found that Lolium perenne and Medicago, which have well-developed roots, exhibited superior slope protection effects. These findings contribute to the theoretical understanding for the implementation of green ecological protection technology on soil slopes.

Wire-arc additive manufacturing (WAAM) technology integrates the characteristics of additive manufacturing and traditional welding technology. It has been found to be capable of forming large-scale metal components with low cost and higher deposition rates. However, it also has potential issues in morphological accuracy, microstructure, and properties due to the arc-based metallurgy mechanisms with complex thermal cycles. This article intends to give a detailed overview of the quality diagnosis and control of the WAAM process, including the formation mechanisms of typical defects, detection approaches, and detection challenges that remain in the WAAM industrial environments. Subsequently, a multisensor data fusion-based closed-loop quality control model is proposed. This model could provide a feasible solution to ensure the quality and deposition efficiency of WAAM parts in complex manufacturing conditions. Based on this model, a novel process named hybrid deposition and micro-rolling (HDMR) is introduced as the green transformation of traditional manufacturing approaches with higher energy efficiency and better quality.

Water pollution containing dyes become increasingly serious environmental problem with the acceleration of urbanization and industrialization process. Renewable adsorbents for cationic dye wastewater treatment are becoming an obstacle because of the difficulty of desorbing the dye from the adsorbent surface after adsorption. To overcome this dilemma, herein, we report a hydrothermal method to fabricate sulfonic acid modified yeast carbon microspheres (SA/YCM). Different characterization techniques like scanning electron microscopy, FTIR spectroscopy, and X-ray diffraction have been used to test the SA/YCM. Decorated with sulfonic acid group, the modified yeast carbon microspheres possess excellent ability of adsorbing positively charged materials. The removal rate of Methyl blue (MB) by renewable adsorbent SA/YCM can reach 85.3% when the concentration is 500 mg/L. The SA/YCM regenerated by HCl showed excellent regeneration adsorption capacity (78.1%) after five cycles of adsorption–desorption regeneration experiment. Adsorption isotherm and kinetic behaviors of SA/YCM for methylene blue dyes removal were studied and fitted to different existing models. Owing to the numerous sulfonic acid groups on the surface, the SA/YCM showed prominent reusability after regeneration under acidic conditions, which could withstand repeated adsorption–desorption cycles as well as multiple practical applications.

Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R2, and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively.

Robot hands play an important role in the interaction between robots and the environment, and the precision and complexity of their tasks in work production are becoming higher and higher. However, because the traditional manipulator has too many driving components, complex control, and a lack of versatility, it is difficult to solve the contradiction between the degrees of freedom, weight, flexibility, and grasping ability. The existing manipulator has difficulty meeting the diversified requirements of a simple structure, a large grasping force, and the ability to automatically adapt to shape when grasping an object. To solve this problem, we designed a kind of underactuated manipulator with a simple structure and strong generality based on the metamorphic mechanism principle. First, the mechanism of the manipulator was designed on the basis of the metamorphic mechanism principle, and a kinematics analysis was carried out. Then, the genetic algorithm was used to optimize the size parameters of the manipulator finger structure. Finally, for different shapes of objects, the design of the control circuit binding force feedback control was carried out with a grasping experiment. The experimental results show that the manipulator has simple control and can grasp objects of different sizes, positions, and shapes.

Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE = 36.3692), determination coefficient (R 2  = 0.9890), mean absolute error (MAE = 9.5246) and peak percentage threshold statistics (PPTS(5) = 0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

The soft rock in Mu Us Sandy Land has rich resources and high content of clay minerals. The combination of soft rock with sand can play a certain role in sand fixation and promote the green development of ecological environment. In this paper, the aeolian sandy soil in Mu Us Sandy was taken as the research object, and it was mixed with soft rock to form composite soil. The four volume ratios of soft rock to sand were respectively 0:1, 1:5, 1:2 and 1:1. And CK, P1, P2 and P3 were used to represent the above four volume ratios in turn. By means of quantitative fluorescent PCR and high throughput sequencing, 16S rRNA gene abundance and community structure were investigated. The results showed that the soil organic carbon (SOC) and total nitrogen (TN) contents in 0-30cm soil layer were higher. Compared with CK, the SOC of P2 was improved by 112.77% and that of P1 was 88.67%. The content of available phosphorus (AP) and available potassium (AK) was higher in 30-60cm soil layer, and P3 was more effective. The abundance of 16S rRNA gene in the mixed soil bacteria ranged from 0.03×10 9 to 0.21×10 9 copies g -1 dry soil, which was consistent with the changes of nutrients. Under different soil layers, the three dominant bacteria in the mixed soil were the same, namely Phylum Actinobacteriota , Phylum Proteobacteria and Phylum Chloroflexi , and there were more unique genera in each soil layer. Both bacteria ɑ and β diversity showed that the community structure of P1 and P3 in 0-30cm soil layers was similar, and that of P1 and P2 in 30-60cm soil layers was similar. AK, SOC, AN (ammonium nitrogen), TN and NN (nitrate nitrogen) were the main factors contributing to the differentiation of microbial community structure under different compound ratios and soil layers, and Phylum Actinobacteria has the largest correlation with nutrients. The results showed that the soft rock could improve the quality of sandy soil, and that the growth of microbial growth was dependent on the soil physicochemical characteristics. The results of this study will be helpful to the study of the microscopical theory for the control of the wind-blown sand and the ecology of the desert.

To combat thermal-induced problems such as residual stress, deformation, and crack, induction heating is introduced into weld-based additive manufacturing process as a controlled thermal intervention. To date, however, numerical simulation of this induction-assisted weld-based additive manufacturing process is still a tough task; for conducting transient thermoelectromagnetic motion, coupling analysis is computationally prohibitive. In this paper, a simulation strategy is devised to address the problem. The coupling analysis is performed only at a typical time to obtain the representative distribution of induction heat, which is then transferred to the thermal analysis of multilayer deposition as a moving heat source. Utilizing this strategy, the effects of real-time induction preheating and postheating on residual stress state are analyzed in comparative simulations. The results show that both induction preheating and postheating lead to more homogeneous heat input and lower residual stresses compared with the case without induction heating.

Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN’s edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.A super-resolution diffractive neural network, featuring super-oscillatory angular responses, can perform all-optical DOA estimation with an angular resolution beyond the diffraction limit, which facilitates low-latency integrated sensing and communication.