Predicting Surface Roughness of Parts Manufactured by the Fused Deposition Modeling Based on Coupled Machine Learning models
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.
万方数据
维普
