Lacki Piotr, Adamus Janina, Lachs Kuba, Lacki Wiktor
Faculty of Civil Engineering, Częstochowa University of Technology, J.H. Dąbrowskiego 69 Str., 42-201 Częstochowa, Poland.
Faculty of Computer Science and Artificial Intelligence, Czestochowa University of Technology, J.H. Dabrowskiego 69 Str., 42-201 Czestochowa, Poland.
Materials (Basel). 2025 Apr 24;18(9):1923. doi: 10.3390/ma18091923.
In this study, Artificial Neural Networks (ANN) were employed to develop a Digital Twin (DT) of the Rotary Friction Welding (RFW) process. The neural network models were trained to predict the peak temperature generated during the welding process of dissimilar Ti Grade 2/AA 5005 joints over a temperature range of 20-640 °C. This prediction was based on a parametric numerical model of the RFW process constructed using the Finite Element Method (FEM) within the ADINA System software. Numerical simulations enabled a detailed analysis of the temperature distribution within the weldment. Accurate temperature predictions are essential for assessing the mechanical properties and microstructural integrity of the welded materials. Artificial Intelligence (AI) models, trained on historical data and real-time inputs, dynamically adjust critical process parameters-such as rotational speed, axial force, and friction time-to maintain optimal weld quality. A key advantage of employing AI-augmented DT systems in the RFW process is the ability to conduct real-time (less than 0.1 s) optimization and adaptive control. By integrating a Genetic Algorithm (GA) with the DT algorithm of the RFW process, the authors developed an effective tool for analyzing parameters such as axial force and rotational speed, in order to determine the optimal welding conditions, which translates into improved joint quality, minimized defects, and maximized process efficiency.
在本研究中，采用人工神经网络（ANN）来开发旋转摩擦焊接（RFW）过程的数字孪生（DT）。对神经网络模型进行训练，以预测不同的Ti 2级/AA 5005接头在20 - 640°C温度范围内焊接过程中产生的峰值温度。该预测基于在ADINA系统软件中使用有限元方法（FEM）构建的RFW过程参数数值模型。数值模拟能够对焊件内的温度分布进行详细分析。准确的温度预测对于评估焊接材料的力学性能和微观结构完整性至关重要。基于历史数据和实时输入进行训练的人工智能（AI）模型，动态调整关键工艺参数，如转速、轴向力和摩擦时间，以保持最佳焊接质量。在RFW过程中采用人工智能增强的DT系统的一个关键优势是能够进行实时（小于0.1秒）优化和自适应控制。通过将遗传算法（GA）与RFW过程的DT算法相结合，作者开发了一种有效的工具来分析轴向力和转速等参数，以确定最佳焊接条件，这转化为提高接头质量、最小化缺陷和最大化工艺效率。
