This research article presents a comprehensive design of operational strategies for robotic warehouses, a novel paradigm distinct from traditional manual and automated warehouse operations. The study introduces three independent strategies that address key operational tasks in robotic warehouses: product replenishment, shelf storage, and order-picking. Recognizing the interdependencies among these tasks, the research proposes a co-optimization approach and synchronization mechanism to harmonize the three strategies. This approach aims to align the strategies towards a common optimization objective, thereby enhancing the overall operational efficiency of the robotic warehouse. The cooperative optimization approach promotes synergy among the operational tasks, while the synchronization mechanism ensures that the tasks are executed in a coordinated manner, minimizing conflicts and maximizing efficiency. The research demonstrates that by integrating these strategies and applying the cooperative optimization and synchronization mechanism, significant improvements can be achieved in the operational efficiency of robotic warehouse.
The motor grader is a high-speed travelling-type earth-moving machine widely used in infrastructure construction, of which the working device is driven by the travelling system to overcome the working load. At present, the power shift gear transmission is the major driving mode of high-power motor graders, yet the application of hydrostatic driving in travelling system is also of great research value and worth continuous exploration due to its unique advantages. In this paper, design of the open-loop and the closed-loop hydrostatic travelling systems of the motor grader is studied and compared by theoretical analysis, simulation and test. Firstly, the structure and the working principle of the two systems are introduced, and the acceleration performance, braking performance and the efficiency are calculated and compared quantitatively. Secondly, the simulation models of these two systems are built in AMESim software, and the simulation are carried out under two typical working conditions, i.e. starting and stopping processes. Next, the difference and the causes are discussed based on the test analysis of the performance of acceleration time, stopping distance, anti-drag phenomenon, peak pressure and suction of both the two systems. Finally, from the perspective of functionality, performance and manufacture cost, the advantages and the disadvantages of the two systems are summarized.
Preliminary research was conducted on the implementation approach and specific practices of aerospace cable process knowledge modeling according to the E3 model. The cable process E3 model application, process knowledge modeling methods and specific implementation cases were analyzed. Establishing a cable process knowledge modeling method based on the matching rules of E3 model data and process knowledge data, accomplishing the intelligent design of aerospace cable process, improving the design quality and efficiency of aerospace cable process models.
The digital twin is a vital technology for the intelligent manufacturing of proton exchange membrane fuel cell (PEMFC) stacks. Aiming to achieve an efficient optimal design for the stack assembly process, a digital twin modeling approach combining finite element analysis (FEA) and artificial neural network (ANN) is proposed in this paper. The establishing method of finite element model (FEM) for the stack under assembly conditions, the dataset construction method for FEM simulation results, and the modeling method of ANN for mechanical behavior prediction are systematically expounded. After that, the digital twin modeling solution is verified by a case study of the contact pressure prediction inside the stack. The results show that the ANN model based on the simulation dataset has a prediction accuracy close to 98% of the FEM, and the computational efficiency is improved more than tens of thousands of times. This model can also quickly obtain the entire stack uniformity at different compression ratios, which is a prerequisite for guiding the optimal design of the stack assembly process in production scenarios.
With the emergence of Smart Product Service Systems (SPSS), the construction of digital twin functional models has become a critical research area. This study aims to propose a comprehensive approach for constructing digital twin functional models specifically for SPSS. Currently, research on SPSS and digital twin technology primarily focuses on models and structures, with limited in-depth analysis of their functional dimensions. To address this gap, we propose a method that combines the functional models from Model-Based Systems Engineering (MBSE) with the flow analysis models, including material flow, information flow, and energy flow. By establishing correlations between the elements of the functional model and the flow model, we develop a comprehensive digital twin functional model. To validate the effectiveness of this method, we apply it to a diesel engine as a case study and successfully construct a comprehensive digital twin functional model for the system. The application of this construction method in the diesel engine case study confirms its effectiveness and feasibility, providing valuable insights for research and practice in the field of SPSS.
