Spanu Paulina, Abaza Bogdan Felician, Constantinescu Teodor Catalin
Manufacturing Engineering Department, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.
General Nursing Assistance Department, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.
Polymers (Basel). 2024 Nov 25;16(23):3283. doi: 10.3390/polym16233283.
Milling parts made from glass fiber-reinforced polymer (GFRP) composite materials are recommended to achieve the geometric shapes and dimensional tolerances required for large parts manufactured using the spray lay-up technique. The quality of the surfaces machined by milling is significantly influenced by the temperature generated in the cutting zone. This study aims to develop an Artificial Neural Network (ANN) model to predict the temperature generated when milling GFRP. The ANN model for temperature prediction was created using a virtual instrument developed in the graphical programming language LabVIEW. Predicting temperature is crucial because excessive heat during milling can lead to several issues, such as tool wear and thermal degradation in the polymer matrix. The temperature in the tool-workpiece contact surface during the milling process was measured using a thermography technique with a ThermaCAM SC 640 camera (provided by FLIR Systems AB, Danderyd, Sweden), and the data were analyzed using the ThermaCAM Researcher Professional 2.8 SR-2 software. Experimental research shows that the cutting speed has a much more significant effect on the temperature in the cutting zone compared to axial depth of cut and feed speed. The maximum temperature of 85.19 °C was measured in the tool-workpiece contact zone during machining at a cutting speed of 75.39 m/min, a feed rate of 250 mm/min, and an axial depth of cut of 12 mm. This temperature rise occurred due to the larger contact area and heightened friction resulting from the abrasive characteristics of the reinforcement material.
建议对由玻璃纤维增强聚合物（GFRP）复合材料制成的零件进行铣削加工，以获得采用喷射铺层技术制造大型零件所需的几何形状和尺寸公差。铣削加工表面的质量受切削区域产生的温度显著影响。本研究旨在开发一种人工神经网络（ANN）模型，以预测铣削GFRP时产生的温度。温度预测的ANN模型是使用图形编程语言LabVIEW开发的虚拟仪器创建的。预测温度至关重要，因为铣削过程中产生的过多热量会导致多种问题，如刀具磨损和聚合物基体的热降解。使用ThermaCAM SC 640相机（由瑞典丹德吕德的FLIR Systems AB公司提供）的热成像技术测量铣削过程中刀具与工件接触表面的温度，并使用ThermaCAM Researcher Professional 2.8 SR-2软件对数据进行分析。实验研究表明，与轴向切削深度和进给速度相比，切削速度对切削区域的温度影响更为显著。在切削速度为75.39 m/min、进给速度为250 mm/min、轴向切削深度为12 mm的加工过程中，刀具与工件接触区域测得的最高温度为85.19°C。这种温度升高是由于增强材料的磨料特性导致接触面积增大和摩擦力增加所致。
