2較高。與ANN模型相比，SVR模型在預(yù)測(cè)單道幾何特性和表面粗糙度方面都有更好的效果。在此基礎(chǔ)上制造了多層薄壁零件，驗(yàn)證了模型的準(zhǔn)確性。;Machine learning prediction models for thin wire-based metal additive manufacturing (MAM) process were proposed, aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness. The effects of laser power, wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied. The results show that laser power has a significant impact on the width of the single track but little effect on the height. As the wire feeding speed increases, the width and height of the single track increase, especially the height. The faster the scanning speed, the smaller the width of the single track, while the height does not change much. Then, support vector regression (SVR) and artificial neural network (ANN) regression methods were employed to set up prediction models. The SVR and ANN regression models perform well in predicting the width, with a smaller root mean square error and a higher correlation coefficient R2. Compared with the ANN model, the SVR model performs better both in predicting geometric characteristics of single track and surface roughness. Multi-layer thin-walled parts were manufactured to verify the accuracy of the models."/>