关键词: 熔融沉积制造, 质量预测, 贝叶斯超频道优化算法, 留一法, BP神经网络

Abstract: Surface roughness and tensile strength are important indicators to measure the quality of Fused Deposition Modeling (FDM) parts.However,FDM process parameters are numerous,and there is a non-linear relationship between FDM process parameters and FDM molded part quality,so it is difficult for traditional methods to predict these two indicators.Therefore,a quality prediction method for FDM 3D printed parts was proposed to improve the prediction accuracy and stability,which combined Bayesian Optimization and Hyperband (BOHB) algorithm and Back Propagation (BP) neural network.The process parameters were used as input to the model,such as layer thickness,number of scans and fill interval.The BOHB algorithm was used to optimize the hyperparameter of BP neural network for obtaining BOHB-BP.Then,the experimental data of surface roughness and tensile strength were obtained by central composite design,and the accuracy and stability of the model were verified according to the leave-one-out method on this data set.The predictions of the model BOHB-BP with the models GA-BP and BP were compared with each other.The above results proved that the proposed method had better prediction accuracy and stability on different datasets.

Key words: fused deposition modeling, quality prediction, Bayesian optimization and hyperband algorithm, leave-one-out method, back propagation neural network