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The topology optimization of dynamic stress constraints is highly nonlinear and singular and has been little studied. Dynamic stress based on progressive structural optimization is only available by applying the modal iteration method, but due to the nonlinear limitations of the modal superposition method, there is an urgent need to develop a progressive structural optimization method based on dynamic stress sensitivity under direct integration. This method is for the dynamic stresses under non-periodic loading with iterative cycle updating variations. This article proposes a topological optimization method of continuum structures with stress constraints under an aperiodic load based on the Bi-directional Evolutionary Structural Optimization Method (BESO). First, the P-norm condensation function was used to obtain the global stress to approximate maximum stress. By introducing the Lagrange multiplier, the design goal was to increase the P-norm stress on the basis of the smallest volume. After that, based on the dynamic finite element theory, the sensitivity of each cell formula of the objective function and the constraint conditions of the design variables were strictly derived. Then, the performance evaluation index was put forward based on volume and stress, and the convergence criterion based on the performance evaluation index was defined. This method solves the topology optimization problem of stress constraints under a non-periodic load and the topology optimization problem of stress constraints under a periodic load, such as a simple harmonic load. [ABSTRACT FROM AUTHOR]

The tribological behavior of 42CrMo4/17NiCrMo6-4 under grease lubrication was explored in terms of load, speed, hardness matching, and lubrication quantity. Optical microscopy, scanning electron microscopy, and a surface profilometer were used to investigate the wear mechanism. The results show that hardness matching has the greatest impact on the wear resistance and friction reduction of the friction pair, followed by the load factor, with the impacts of speed and lubricant quantity being minor. Increasing the hardness of 42CrMo4 reduces the friction coefficient and wear volume of the friction pair substantially. When the maximum surface hardness of 42CrMo4 was compared with the lowest surface hardness, the friction coefficient was reduced by 21.5%, and the wear volume was reduced by 87.2%. Abrasive wear is the sort of wear failure that was seen, and as the hardness of 42CrMo4 increased, more severe fatigue wear appeared on 17NiCrMo6-4. While the wear volume initially increases and subsequently lowers with increasing load, the friction coefficient initially decreases and then stabilizes. A synergistic combination of abrasive and adhesive wear occurs under high load, changing the wear type from abrasive wear under low load. The wear volume is decreased by the sticky layer generated under high load conditions, which achieves superior wear prevention. This study is anticipated to offer recommendations for designing gears' required hardness under various operating circumstances. [ABSTRACT FROM AUTHOR]

This study proposes a remaining useful life (RUL) prediction method using limited degradation data with an unknown degradation model for hydraulic pumps with long service lives and no failure data in turbine control systems. The volumetric efficiency is calculated based on real-time monitoring signal data, and it is used as the degradation indicator. The optimal degradation curve is established using the degradation trajectory model, and the optimal probability distribution model is selected via the K-S test. The above process was repeated to optimize the degradation model and update parameters in different performance degradation stages of the hydraulic pump, providing quantification of the prediction uncertainty and enabling accurate online prediction of the hydraulic pump's RUL. Finally, an RUL test bench for hydraulic pumps is built for verification. The results show that the proposed method is convenient, efficient, and has low model complexity. The method enables online accurate prediction of the RUL of hydraulic pumps using only limited degradation data, with a prediction accuracy of over 85%, which meets practical application requirements. [ABSTRACT FROM AUTHOR]

Featured Application: The pressure-control method based on multistage orifices breaks the traditional scheme of using pressure valves or directly using system pressure to realize pilot control pressure. The method has been successfully applied in electrohydraulic control systems of low-pressure steam turbine, a main equipment of power generation industry. The interaction of pressure and flow in a hydraulic system with multiple working conditions, multiple actuators, and large flow limits action adjustment and control. Through a pilot pressure control circuit, hydraulically operated valves can adjust pressure or direction more effectively. A recent study proposed a two-stage pressure control method based on multistage orifices and solenoid valves. The requirements of the number and diameter ratio of short orifices in the series to realize the two-stage pressure control were theoretically analyzed. Scheme design and experiment were carried out. The influence of structures of complex flow channel and solenoid valve on the higher or lower pilot control pressure was considered in the experiment. The method was experimentally verified and successfully applied in a turbine electrohydraulic control system with lower maintenance costs, making the system more reliable in the case of electrical failure. Research results provide insight into pilot pressure control in fluid systems using multistage orifices to achieve either higher or lower pressure. In addition, it has important guiding significance for the design of valves or engineering systems based on pilot hydraulic pressure. [ABSTRACT FROM AUTHOR]

Biological structures have excellent mechanical performances including lightweight, high stiffness, etc. However, these are difficult to apply directly to some given complex structures, such as automobile frame, control arm, etc. In this study, a novel bionic design method for skeleton structures with complex features is proposed by the bio-inspired idea of "main-branch and sub-branch". The envelope model of a given part is established by analyzing the structural functions and working conditions, and the load path is extracted by the load-transferred law as the structural main-branch. Then, the selection criterion of bionic prototype is established from three aspects: load similarity, structural similarity and manufacturability. The cross-sections with high similarities are selected as the structural sub-branch. Finally, the multi-objective size optimization is carried out and a new model is established. The bionic design of a control arm is carried out by the method: structural main-branch is obtained by the load path analysis and structural sub-branch is occupied by the fish-bone structure. The design result shows that the structural stiffness is increased by 62.3%, while the weight is reduced by 24.75%. The method can also be used for other fields including automobile, aerospace and civil engineering. [ABSTRACT FROM AUTHOR]

Ti-CFRP-Ti laminated stacks have been widely used in aviation, aerospace, shipbuilding and other industries, owing to its excellent physical and electrochemical properties. However, chip blockages occur easily when drilling into Ti-CFRP-Ti laminated stacks, resulting in a rapid rise of drilling temperature and an increase of axial drilling force, which may lead to the intensification of tool wear and a decline of drilling quality. Cutting force signals can effectively reflect the drilling process and tool condition, however, the traditional plate dynamometer is typically difficult in realizing the follow-up online measurement. Therefore, an intelligent tool holder system for real-time sensing of the cutting force is developed and constructed in this paper, and the variable parameter drilling method of Ti-CFRP-Ti laminated stacks is studied on this basis. Firstly, an intelligent tool holder system with high flexibility and adaptability is designed; Secondly, a cutting force signal processing method based on compressed sensing (CS) theory is proposed to solve the problem of high-frequency signal transmission; Lastly, the drilling experiment of Ti-CFRP-Ti laminated stacks is carried out based on the intelligent tool holder system, and the drilling parameters are optimized using a compromise programming approach and analytic hierarchy process (AHP). The comparison of results show that the optimized drilling parameters can effectively reduce the hole wall surface roughness and improve the drilling efficiency while ensuring a small axial force. [ABSTRACT FROM AUTHOR]

Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as without good hyper-parameter values the performance of these algorithms will be very poor. Unfortunately, for complex machine learning models like deep neural networks, it is very difficult to determine their hyper-parameters. Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization. In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning algorithms. This method is defined as the Tabu_Genetic Algorithm. In order to verify the performance of the proposed algorithm, two sets of contrast experiments are conducted. The Tabu_Genetic Algorithm and other four methods are simultaneously used to search for good values of hyper-parameters of deep convolutional neural networks. Experimental results show that, compared to Random Search and Bayesian optimization methods, the proposed Tabu_Genetic Algorithm finds a better model in less time. Whether in a low-dimensional or high-dimensional space, the Tabu_Genetic Algorithm has better search capabilities as an effective method for finding the hyper-parameters of learning algorithms. The presented method in this paper provides a new solution for solving the hyper-parameters optimization problem of complex machine learning models, which will provide machine learning algorithms with better performance when solving practical problems. [ABSTRACT FROM AUTHOR]