Asadzadeh Mohammad Zhian, Roppert Klaus, Raninger Peter
Materials Center Leoben Forschung GmbH (MCL), Roseggerstraße 12, 8700 Leoben, Austria.
Institute of Fundamentals and Theory of Electrical Engineering, Technical University of Graz, Inffeldgasse 18/I, 8010 Graz, Austria.
Materials (Basel). 2023 Jul 15;16(14):5013. doi: 10.3390/ma16145013.
Physics-Informed neural networks (PINNs) have demonstrated remarkable performance in solving partial differential equations (PDEs) by incorporating the governing PDEs into the network's loss function during optimization. PINNs have been successfully applied to diverse inverse and forward problems. This study investigates the feasibility of using PINNs for material data identification in an induction hardening test rig. By utilizing temperature sensor data and imposing the heat equation with initial and boundary conditions, thermo-physical material properties, such as specific heat, thermal conductivity, and the heat convection coefficient, were estimated. To validate the effectiveness of the PINNs in material data estimation, benchmark data generated by a finite element model (FEM) of an air-cooled cylindrical sample were used. The accurate identification of the material data using only a limited number of virtual temperature sensor data points was demonstrated. The influence of the sensor positions and measurement noise on the uncertainty of the estimated parameters was examined. The study confirms the robustness and accuracy of this approach in the presence of measurement noise, albeit with lower efficiency, thereby requiring more time to converge. Lastly, the applicability of the presented approach to real measurement data obtained from an air-cooled cylindrical sample heated in an induction heating test rig was discussed. This research contributes to the accurate offline estimation of material data and has implications for optimizing induction heat treatments.
物理信息神经网络（PINNs）通过在优化过程中将控制偏微分方程（PDEs）纳入网络的损失函数，在求解偏微分方程方面展现出卓越的性能。PINNs已成功应用于各种正反问题。本研究探讨了在感应淬火试验台上使用PINNs进行材料数据识别的可行性。通过利用温度传感器数据并施加带有初始条件和边界条件的热方程，估算了诸如比热容、热导率和热对流系数等热物理材料特性。为了验证PINNs在材料数据估计中的有效性，使用了由风冷圆柱形样品的有限元模型（FEM）生成的基准数据。展示了仅使用有限数量的虚拟温度传感器数据点就能准确识别材料数据。研究了传感器位置和测量噪声对估计参数不确定性的影响。该研究证实了这种方法在存在测量噪声时的稳健性和准确性，尽管效率较低，因此需要更多时间来收敛。最后，讨论了所提出的方法对从感应加热试验台上加热的风冷圆柱形样品获得的实际测量数据的适用性。这项研究有助于准确地离线估计材料数据，并对优化感应热处理具有重要意义。
