Quarto Mariangela, D'Urso Gianluca, Giardini Claudio, Maccarini Giancarlo, Carminati Mattia
Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7/b, 24044 Dalmine, Italy.
Micromachines (Basel). 2021 Jun 7;12(6):667. doi: 10.3390/mi12060667.
Artificial Neural Network (ANN), together with a Particle Swarm Optimization (PSO) and Finite Element Model (FEM), was used to forecast the process performances for the Micro Electrical Discharge Machining (micro-EDM) drilling process. The integrated ANN-PSO methodology has a double direction functionality, responding to different industrial needs. It allows to optimize the process parameters as a function of the required performances and, at the same time, it allows to forecast the process performances fixing the process parameters. The functionality is strictly related to the input and/or output fixed in the model. The FEM model was based on the capacity of modeling the removal process through the mesh element deletion, simulating electrical discharges through a proper heat-flux. This paper compares these prevision models, relating the expected results with the experimental data. In general, the results show that the integrated ANN-PSO methodology is more accurate in the performance previsions. Furthermore, the ANN-PSO model is faster and easier to apply, but it requires a large amount of historical data for the ANN training. On the contrary, the FEM is more complex to set up, since many physical and thermal characteristics of the materials are necessary, and a great deal of time is required for a single simulation.
人工神经网络（ANN）与粒子群优化算法（PSO）和有限元模型（FEM）一起，被用于预测微细电火花加工（micro-EDM）钻孔工艺的加工性能。集成的ANN-PSO方法具有双向功能，可满足不同的工业需求。它能够根据所需性能优化工艺参数，同时，在设定工艺参数时，它还能预测加工性能。该功能与模型中设定的输入和/或输出密切相关。有限元模型基于通过网格单元删除对去除过程进行建模的能力，通过适当的热通量模拟放电过程。本文比较了这些预测模型，并将预期结果与实验数据相关联。总体而言，结果表明集成的ANN-PSO方法在性能预测方面更为准确。此外，ANN-PSO模型应用起来更快、更简便，但它需要大量的历史数据用于人工神经网络训练。相反，有限元模型的建立更为复杂，因为需要材料的许多物理和热特性，并且单次模拟需要大量时间。
