摘要
玻璃纤维增强复合材料(Glass fiber-reinforced polymer,GFRP)是一种广泛应用的高性能复合材料,因此也对不同加工工艺提出了更高的要求。为研究其加工性能,以主轴转速(9000~15000 r/min)、进给速度(1500~4500 mm/min)以及切削深度(0.1~0.5 mm)为切削参数进行试验,以铣削过程中的切削力以及最终加工质量作为衡量指标,使用线性拟合建立切削力数学模型,以遗传算法优化的BP神经网络对加工表面粗糙度进行预测,最后对高效铣削加工工艺进行多目标优化。切削力均随着进给速度、主轴转速以及切削深度而增加,切削参数对表面粗糙度为复杂的非线性相关,表面粗糙度同切削深度呈现出明显的正相关;对切削力进行的线性回归拟合表现出较好的拟合准度以及预测精度,使用遗传算法优化后的神经网络可以很好地提高对于表面粗糙度的预测精度,平均预测误差为8.27%。以较小的切削力,较好的表面质量以及较大的材料去除率对GFRP的加工进行多目标优化,得到高效加工参数为进给速度4500 mm/s,主轴转速11181 r/min,切削深度为0.5 mm,以期为GFRP的高效切削提供参考和指导。
Glass fibre-reinforced polymer(GFRP)is a widely used high-performance composite material,which puts forward higher requirements for different processing technologies.In this paper,the spindle speed(9000~15000 r/min),feed rate(1500~4500 mm/min)and cutting depth(0.1~0.5 mm)were used as cutting parameters to perform tests,with the cutting force during milling and the final machined surface quality as metrics,the mathematical model of cutting force established using linear fitting,the BP neural network optimized with the genetic algorithm used to predict the surface roughness,followed by the final multi-objective optimization of the high-efficiency milling processing technology.The cutting force increased with the feed rate,spindle speed and cutting depth;meanwhile,the cutting parameters had a complex nonlinear correlation with the surface roughness,and the surface roughness had a significant positive correlation with the cutting depth.The linear regression fitting of the cutting force showed good fitting accuracy and prediction accuracy.The neural network optimized with the genetic algorithm could significantly improve the prediction accuracy of surface roughness,with an average prediction error of 8.27%.The multi-objective optimization of the machining of GFRP was carried out with a smaller cutting force,better surface quality and a large material removal rate;the high-efficiency machining parameters were obtained as the feed rate of 4500 mm/s,the spindle speed of 11181 r/min,and the cutting depth of 0.5 mm.
作者
袁向东
伍占文
林雨斌
张丰
丁建文
YUAN Xiang-dong;WU Zhan-wen;LIN Yu-bin;ZHANG Feng;DING Jian-wen(LEUCO Precision Tooling(Taicang) Co.,Ltd.,Suzhou Jiangsu 215400,China;College of Materials Science and Engineering,Nanjing Forestry University,Nanjing Jiangsu 210037,China)
出处
《林业机械与木工设备》
2022年第7期47-55,共9页
Forestry Machinery & Woodworking Equipment
基金
国家自然科学基金“基于热力耦合的WPC高速铣削积屑瘤形成特性及其表面损伤控制研究”(31971594)。