Optimizing the fixture layout of the locating element is an important method to reduce the clamping deformation of thin-walled parts. A method for optimizing the fixture layout based on whale optimization algorithm is proposed in this paper, the number and positions of the fixtures for curved thin-walled parts are optimized. Firstly, the multi-point flexible locating tooling for curved thin-walled parts is developed based on the multi-point support technology. Then the strain energy is used to describe the deformation of the curved thin-walled parts in all directions, and an optimization model that takes the position of the locating element as a decision variable and minimum strain energy as the goal is established. Combined with the whale optimization algorithm and the parameterized finite element analysis, the optimal design of the number and positions of fixture locators for curved thin-walled parts are realized. Finally, the effectiveness of the proposed method is validated by the aircraft skin locating layout optimization, and a multi-point flexible locating and deformation measurement platform is constructed to verify the results of finite element calculations.
Solid backfilling in coal mining refers to filling the goaf with solid materials to form a support structure, ensuring safety in the ground and upper mining areas. This mining method maximizes coal production and addresses environmental requirements. However, in traditional backfill mining, challenges exist, such as limited perception variables, independent sensing devices, insufficient sensing data, and data isolation. These issues hinder the real-time monitoring of backfilling operations and limit intelligent process development. This paper proposes a perception network framework specifically designed for key data in solid backfilling operations to address these challenges. Specifically, it analyses critical perception objects in the backfilling process and proposes a perception network and functional framework for the coal mine backfilling Internet of Things (IoT). These frameworks facilitate rapidly concentrating key perception data into a unified data centre. Subsequently, the paper investigates the assurance of data validity in the perception system of the solid backfilling operation within this framework. Specifically, it considers potential data anomalies that may arise from the rapid data concentration in the perception network. To mitigate this issue, a transformer-based anomaly detection model is proposed, which filters out data that does not reflect the true state of perception objects in solid backfilling operations. Finally, experimental design and validation are conducted. The experimental results demonstrate that the proposed anomaly detection model achieves an accuracy of 90%, indicating its effective detection capability. Moreover, the model exhibits good generalization ability, making it suitable for monitoring data validity in scenarios involving increased perception objects in solid backfilling perception systems.
Aiming at the problem of the inefficiency of coal mine water reuse, a multi-level scheduling method for mine water reuse based on an improved whale optimization algorithm is proposed. Firstly, the optimization objects of mine water reuse time and reuse cost are used to establish the optimal scheduling model of mine water. Secondly, in order to overcome the defect that the whale optimization algorithm (WOA) is prone to local convergence, the opposition-based learning strategy is introduced to speed up the convergence speed, the Levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimization, the nonlinear convergence factor is used to balance the global and local search ability, and the adaptive inertia weight is used to improve the optimization accuracy of the algorithm. Finally, the improved whale optimization algorithm (IWOA) is applied to the mine water optimization scheduling model with multiple objects and constraints. The results show that the reuse efficiency of the multi-level scheduling method of mine water reuse is increased by 30.2% and 31.9%, respectively, in the heating and nonheating seasons, which can significantly improve the reuse efficiency of mine water and realize the efficient utilization of mine water reuse deployment. At the same time, experiments show that the improved whale optimization algorithm has higher convergence accuracy and speed, which proves the feasibility and superiority of its improvement strategies.
Aiming at the characteristics of low sensitivity and narrow frequency range of existing microseismic monitoring sensors for mine water hazard prevention and control, a piezoelectric acceleration sensor for microseismic monitoring based on a kind of triangular shear structure is proposed. Firstly, the structure of the triangular shear piezoelectric acceleration sensor is designed, and its dynamic model is built. The structural and material parameters related to natural frequency and sensitivity are analyzed. Then, the selection of piezoelectric ceramic materials is discussed. The parametric design of the designed sensor is carried out, and its finite element structural model is built by ANSYS. The modal analysis, resonance response analysis, and piezoelectric analysis of the designed sensor are carried out. The simulation results indicate that the working frequency and sensitivity of the designed sensor meet the requirements of microseismic monitoring. Response surface optimization is adopted to analyze the influence of sensor element design variables on the sensitivity and resonant frequency of the designed sensor. The reoptimized design of the reference sensor improves the resonant frequency of the designed sensor by 9.46% and the charge sensitivity by 18.96%. Finally, the designed sensor is calibrated, and the microseismic signal detection experiment is carried out. The results indicate that the resonant frequency of the designed sensor is 6150 Hz, the working frequency is 0.1-2050 Hz, and the charge sensitivity is 1600 pC/g. The sensor can detect microseismic signals with a wide frequency range and high sensitivity.
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