SLM (Selective Laser Melting) is a unique additive manufacturing technology which plays an irreplaceable role in the modern industrial revolution. 3D printers can directly process metal powder quickly to obtain the necessary parts faster. Shortly, it will be possible to manufacture products at unparalleled speeds. Advanced manufacturing technology is used to produce durable and efficient parts with different metals that have good metal structure performance and excellent metal thermal performance, to lead the way for laser powder printing technology. Traditional creative ways are usually limited by time, and cannot respond to customers’ needs fast enough; for some parts with high precision and complexity, conventional manufacturing methods are inadequate. Contrary to this, SLM technology offers some advantages, such as requiring no molds this decreases production time and helps to reduce costs. In addition, SLM technology has strong comprehensive functions, which can reduce assembly time and improve material utilization. Parts with complex structures, such as cavities and three-dimensional grids, can be made without restricting the shape of products. Products or parts can be printed quickly without the use of expensive production equipment. The product quality is better, and the mechanical load performance is comparable to traditional production technologies (such as forging). This paper introduces in detail the process parameters that affect SLM technology and how they affect SLM, commonly used metal materials and non-metallic materials, and summarizes the current research. Finally, the problems faced by SLM are prospected.
In a high-precision servo system, the nonlinear friction link is the key factor affecting the system performance. Reasonable solving of the friction link in servo systems has become a focus of current research. This paper summarizes the friction nonlinearity that affects the control performance of servo systems. First, the characteristics of friction are summarized, and the advantages and disadvantages of typical friction models in recent years are analyzed. Subsequently, existing friction model parameter identification methods are introduced and evaluated. On this basis, the development level of the friction nonlinear control strategy is analyzed from three aspects: friction model-based control, friction model-free control, and compound control. Finally, the objective advantages and disadvantages of the existing technology are summarized, and the future development direction of the friction model and selection reference for the nonlinear friction control strategy are comprehensively discussed.
Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.
Aiming at the problem that auto disturbance rejection controller (ADRC) requires too many tuning parameters, this paper proposed an improved grey wolf optimizer algorithm to tune the parameters of ADRC, and used the ADRC with tuned parameters to control the electro-hydraulic position servo system. Based on the original grey wolf optimizer algorithm (GWO), the linear convergence factor was improved to a non-linear mode to optimize the optimization path, and according to the parameter adjustment advantages of particle swarm optimization, the learning factors were introduced in the process of updating the position to give the wolves consciousness to avoid local optima and improve the convergence speed. Through the test functions, simulation and experimental tests, it was found that the improved grey wolf optimizer had higher convergence accuracy, and the ADRC under the improved grey wolf optimizer parameters tuning could achieve the anti-interference control effect well.
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