摘要
熔融沉积工艺(FDM)制造的零件表面粗糙度高,不仅影响了零件外观,还降低了性能.采用响应面实验设计,研究了层高(A)、填充密度(B)、喷嘴温度(C)、床层温度(D)和打印速度(E)对聚乳酸(PLA)零件表面粗糙度的影响.同时,将遗传算法(GA)与决策树(DT)、人工神经元网络(ANN)两种机器学习模型相结合,预测了零件的表面粗糙度.结果表明,A、B、C和E是显著影响零件表面粗糙度的主效应,A×B、A×C、A×E、B×C、B×E、C×E是影响显著的交互效应.GA+DT耦合模型预测PLA零件表面粗糙度的准确性更高,预测值与实验值的相关系数(R2)、均方误差(MSE)和平均绝对误差(MAE)分别为0.952、0.132 和0.234,优于GA+ANN的0.823、1.561 和1.759.GA+DT模型的预测值与实验值的Pearson相关系数为0.984,而GA+ANN模型仅为0.903,这表明GA+DT模型在预测PLA零件表面粗糙度时准确度更高.
Abstract
The surface roughness of parts manufactured by the fused deposition modeling(FDM)is high,which affects the appearance of the parts and decreases the performances.The response surface design was used to investigate the effects of layer height(A),filling density(B),nozzle temperature(C),bed temperature(D),and printing speed(E)on the surface roughness of polylactic acid(PLA)parts.At the same time,combining genetic algorithm(GA)with decision tree(DT)and artificial neural network(ANN),the surface roughness of the parts was predicted.The results show that A,B,C,and E have significant impacts on the surface roughness of parts,A×B,A×C,A×E,B×C,B×E,C×E are significant interaction effects.The GA+DT coupled model has higher accuracy in predicting the surface roughness of PLA parts,and the correlation coefficient(R2),mean square error(MSE),and mean absolute error(MAE)values between predicted and experimental values are respectively 0.952,0.132,and 0.234,which are better than these of GA+ANN coupled model(0.823,1.561,and 1.759).The Pearson correlation coefficient between the predicted values by the GA+DT coupled model and the experimental results is 0.984,while that between the predicted values by the GA+ANN coupled model and the experimental results is 0.903,indicating that the GA+DT coupled model has higher accuracy in predicting the surface roughness of PLA parts.
基金项目
山西省自然科学基金青年基金(202203041345225)
维普
万方数据