工艺参数对FFF制造碳纤维增强尼龙零件力学性能的影响与预测  

Effect and Prediction of Process Parameters on the Mechanical Properties of Carbon-Fiber-Reinforced Nylon Parts Manufactured via FFF

作　　者：李飞飞 蔡金明 程玉华 蔡艺瑞 LI Feifei;CAI Jinming;CHENG Yuhua;CAI Yirui(School of Management Science and Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Ye Construction Group Co.Ltd.,Yancheng 224000,China;Business School,Sanda University,Shanghai 201209,China;Business School,Shenyang Pharmaceutical University,Shenyang 110016,China)

机构地区：[1]南京信息工程大学管理工程学院,江苏南京210044 [2]江苏业建建设集团有限公司,江苏盐城224000 [3]上海杉达学院商学院,上海201209 [4]沈阳药科大学工商学院,辽宁沈阳110016

出　　处：《塑料工业》2024年第4期97-103,共7页China Plastics Industry

基　　金：江苏省研究生教育教学研究资助项目(JGKT22_C049)。

摘　　要：以熔丝制造(FFF)技术加工了碳纤维增强尼龙零件,采用田口试验设计研究了填充图案类型、填充密度、层高和打印方向对零件拉伸强度的影响,利用径向基网络(RBF)预测了不同工艺条件下零件拉伸强度。基于信噪比分析发现,在研究的4个试验因素中,填充密度对零件拉伸强度的影响最显著,其他因素的影响有如下顺序:打印方向>层高>填充图案类型。熔丝制造最优参数水平组合为:填充密度100%,打印方向0°,层高0.1 mm,填充图案类型六边形,在最优参数组合下,零件拉伸强度试验值为114.09 MPa,高于方差分析的回归方程预测值(108.18 MPa)。当径向基网络中隐含层神经元个数为56时,预测零件拉伸强度的均方误差(MSE)最低,为0.003 2,预测最优参数组合下零件拉伸强度为113.24 MPa。径向基网络预测值与试验值的MSE为0.46,回归方程预测值与试验值的MSE则达到20.01,表明径向基网络预测熔丝制造技术加工的零件拉伸强度更准确。Carbon-fiber-reinforced nylon parts were processed by using fused filament fabrication(FFF).The effects of fill pattern,fill density,layer height and print direction on the tensile strength of the parts were studied by using the taguchi experimental design.The tensile strength of the parts under different process conditions was predicted by using the radial basis function network(RBF).Based on the signal-to-noise ratio analysis,among the four experimental factors,the fill density has the most significant impact on the tensile strength of the parts.The other influence factors have the following order:print direction>layer height>fill pattern type.The optimal horizontal parameter combination for fused filament fabrication is shown as follows:a fill density of 100%,a print direction of 0°,a layer height of 0.1 mm,a hexagonal fill pattern.With the optimal parameter combination,the tensile strength of the part is 114.09 MPa,which is higher than the predicted value based on regression equation derived from the analysis of variance(108.18 MPa).When the number of hidden layer neurons in the RBF is 56,the mean square error(MSE)for predicting the tensile strength of the part is the lowest(0.0032).The predicted tensile strength of the part is 113.24 MPa by the RBF under the optimal parameter combination.The MSE between the predicted and experimental values by the RBF is 0.46,while the MSE between the predicted and experimental values by the regression equation is 20.01,indicating that the RBF is more accurate in predicting the tensile strength of parts processed by FFF.

关 键 词：熔丝制造 碳纤维增强尼龙 田口试验设计 径向基网络 拉伸强度 

分 类 号：TQ320.66[化学工程—合成树脂塑料工业]