WAAM has been proven a promising alternative to fabricate medium and large scale metal parts with a high depositing rate and automation level. However, the production quality may deteriorate due to the poor deposited layer surface quality. In this paper, a laser sensor based surface roughness measuring method was developed for WAAM. To improve the surface integrity of deposited layers by WAAM, different machine learning models, including ANFIS, ELM and SVR, were developed to predict the surface roughness. Furthermore, the ANFIS model was optimized by GA and PSO algorithms. Full factorial experiments were conducted to obtain the training data, and the K-fold Cross-validation strategy was applied to train and validate machine learning models. The comparison results indicate that GA–ANFIS has superiority in predicting surface roughness. The RMSE, \( R^{2} \), MAE and MAPE for GA–ANFIS were 0.0694, 0.93516, 0.0574, 14.15% respectively. This study could also provide inspiration and guidance for surface roughness modelling in multipass arc welding and cladding.

WAAM被证明是具有高沉积速率和自动化水平的制造中型和大型金属零件的有希望的替代方法。然而，由于不良的沉积层表面质量，生产质量可能劣化。本文针对WAAM开发了一种基于激光传感器的表面粗糙度测量方法。为了通过WAAM提高沉积层的表面完整性，开发了各种机器学习模型（包括ANFIS，ELM和SVR）来预测表面粗糙度。此外，通过GA和PSO算法对ANFIS模型进行了优化。进行了充分的阶乘实验以获得训练数据，并将K折交叉验证策略应用于训练和验证机器学习模型。比较结果表明，GA-ANFIS在预测表面粗糙度方面具有优势。RMSE，\（R ^ {2} \），GA–ANFIS的MA​​E和MAPE分别为0.0694、0.93516、0.0574、14.15％。该研究还可以为多道弧焊和熔覆中的表面粗糙度建模提供启发和指导。