Agricultural machinery intelligence is the inevitable direction of agricultural machinery design, and the systems in these designs are important tools. In this paper, to address the problem of low processing power of traditional agricultural machinery design systems in analyzing data, such as fit, tolerance, interchangeability, and the assembly process, as well as to overcome the disadvantages of the high cost of intelligent design modules, lack of data compatibility, and inconsistency between modules, a novel agricultural machinery intelligent design system integrating image processing and knowledge reasoning is constructed. An image-processing algorithm and trigger are used to detect the feature parameters of key parts of agricultural machinery and build a virtual prototype. At the same time, a special knowledge base of agricultural machinery is constructed to analyze the test data of the virtual prototype. The results of practical application and software evaluation of third-party institutions show that the system improves the efficiency of intelligent design in key parts of agricultural machinery by approximately 20%, reduces the operation error rate of personnel by approximately 40% and the consumption of computer resources by approximately 30%, and greatly reduces the purchase cost of intelligent design systems to provide a reference for intelligent design to guide actual production.

To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based on deep reinforcement learning. The ideas of target attraction and obstacle rejection of the artificial potential field method are introduced into the distributed proximal policy optimization algorithm, and the APF-DPPO learning model is established. To solve the range repulsion problem of the artificial potential field method, which affects the optimal driving strategy, this paper proposes a directional penalty function method that combines collision penalty and yaw penalty to convert the range penalty of obstacles into a single directional penalty, and establishes the vehicle motion collision model. Finally, the APF-DPPO learning model is selected to train the driving strategy for the virtual vehicle, and the transfer learning method is selected to verify the comparison experiment. The simulation results show that the completion rate of the virtual vehicle in the obstacle environment that generates penalty feedback is as high as 96.3%, which is 3.8% higher than the completion rate in the environment that does not generate penalty feedback. Under different reward functions, the method in this paper obtains the highest cumulative reward value within 500 s, which improves 69 points compared with the reward function method based on the artificial potential field method, and has higher adaptability and robustness in different environments. The experimental results show that this method can effectively improve the efficiency of autonomous driving strategy learning and control the virtual vehicle for autonomous driving behavior decisions, and provide reliable theoretical and technical support for real vehicles in autonomous driving decision-making.

Circular braiding of large-sized and complex irregular structural components is widely used in fields such as aerospace and rail transit. However, existing braiding process design and simulation algorithms lack applicability to mandrels with complex features, such as flanges, concave corners, holes, and hollows, which also have low calculation efficiency in the braiding process simulation. This study first proposes an optimization algorithm for the complex features on the mandrel surface, improving the applicability for the braiding process design of complex and irregular mandrels. Then the mandrel mesh was reconstructed, greatly improving the computational efficiency of the braiding process design process. Finally, a fast iteration method based on the mandrel reconstructed mesh was proposed to achieve the fast calculation of process control parameters, including traction trajectory and traction speed, as well as the prediction of forming quality for the braiding angle and coverage rate. The experiment shows that the algorithm improves the applicability of the multi-braiding angle process and shortens the sampling cycle of braiding process design by more than 60%.

The agricultural machinery experiment is restricted by the crop production season. Missing the crop growth cycle will extend the machine development period. The use of virtual reality technology to complete preassembly and preliminary experiments can reduce the loss caused by this problem. To improve the intelligence and stability of virtual assembly, this paper proposed a more stable dynamic gesture cognition framework: the TCP/IP protocol constituted the network communication terminal, the leap motion-based vision system constituted the gesture data collection terminal, and the CNN-LSTM network constituted the dynamic gesture recognition classification terminal. The dynamic gesture recognition framework and the harvester virtual assembly platform formed a virtual assembly system to achieve gesture interaction. Through experimental analysis, the improved CNN-LSTM network had a small volume and could quickly establish a stable and accurate gesture recognition model with an average accuracy of 98.0% (±0.894). The assembly efficiency of the virtual assembly system with the framework was improved by approximately 15%. The results showed that the accuracy and stability of this model met the requirements, the corresponding assembly parts were robust in the virtual simulation environment of the whole machine, and the harvesting behaviour in the virtual reality scene was close to the real scene. The virtual assembly system under this framework provided technical support for unmanned farms and virtual experiments on agricultural machinery.

Three-dimensional (3D) braided composites are extensively utilized in aerospace laminated composites owing to their superior out-of-plane shear properties and interlaminar delamination resistance compared with two-dimensional (2D) textile laminated composites. However, existing laminate models remain incapable of elucidating the enhancement mechanisms underlying the superior out-of-plane shear properties of 3D braided architectures. Understanding the influence of 3D braided yarn architecture on out-of-plane failure mechanisms is critical for enabling rapid design and batch-efficient manufacturing of aerospace composite components. This study has established a refined mechanical model for 3D braided laminated composites by integrating Hashin3D failure criterion with cohesive traction-separation law. The model quantitatively decoupled the respective contributions of braided architecture and interlaminar interfaces to out-of-plane failure. Results reveal that 3D braided laminated composites exhibit mixed failure modes of buckling and delamination, whereas 2D textile laminated composites failed predominantly through interfacial delamination. This divergence stems from the reduced interlaminar interfaces in 3D braided laminated composites mitigated catastrophic delamination risks. Concurrently, the interlaminar interlacing yarn architecture suppresses interfacial cracks propagation thereby enhancing out-of-plane properties.