Kaliński Krzysztof J, Galewski Marek A, Stawicka-Morawska Natalia, Mazur Michał, Parus Arkadiusz
Faculty of Mechanical Engineering and Ship Technology, Gdańsk University of Technology, 80-233 Gdańsk, Poland.
Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology in Szczecin, 70-310 Szczecin, Poland.
Sensors (Basel). 2022 Jul 7;22(14):5099. doi: 10.3390/s22145099.
This paper concerns the problem of vibration reduction during milling. For this purpose, it is proposed that the standard supports of the workpiece be replaced with adjustable stiffness supports. This affects the modal parameters of the whole system, i.e., object and its supports, which is essential from the point of view of the relative tool-workpiece vibrations. To reduce the vibration level during milling, it is necessary to appropriately set the support stiffness coefficients, which are obtained from numerous milling process simulations. The simulations utilize the model of the workpiece with adjustable supports in the convention of a Finite Element Model (FEM) and a dynamic model of the milling process. The FEM parameters are tuned based on modal tests of the actual workpiece. For assessing simulation results, the proper indicator of vibration level must be selected, which is also discussed in the paper. However, simulating the milling process is time consuming and the total number of simulations needed to search the entire available range of support stiffness coefficients is large. To overcome this issue, the artificial intelligence salp swarm algorithm is used. Finally, for the best combination of stiffness coefficients, the vibration reduction is obtained and a significant reduction in search time for determining the support settings makes the approach proposed in the paper attractive from the point of view of practical applications.
本文关注铣削过程中的减振问题。为此，建议将工件的标准支撑替换为可调刚度支撑。这会影响整个系统的模态参数，即物体及其支撑，从刀具与工件相对振动的角度来看这至关重要。为了降低铣削过程中的振动水平，有必要适当地设置支撑刚度系数，这些系数是通过大量铣削过程模拟获得的。模拟采用有限元模型（FEM）惯例下带有可调支撑的工件模型以及铣削过程的动态模型。有限元模型参数基于实际工件的模态测试进行调整。为了评估模拟结果，必须选择合适的振动水平指标，本文也对此进行了讨论。然而，模拟铣削过程耗时，并且搜索支撑刚度系数的整个可用范围所需的模拟总数很大。为了克服这个问题，使用了人工智能樽海鞘群算法。最后，对于刚度系数的最佳组合，实现了减振，并且确定支撑设置的搜索时间显著减少，从实际应用的角度来看，本文提出的方法很有吸引力。
