基于ACS-DBN的电弧增材制造焊道尺寸预测

Weld Bead Size Prediction of Wire and Arc Additive Manufacturing Based on ACS-DBN

焊道重叠是电弧增材制造(Wire and Arc Additive Manufacturing,WAAM)技术的本质,合适的工艺参数选择对于控制焊道几何形状,提升成型零件的尺寸精度具有重要的意义,为此提出一种基于自适应布谷鸟搜索(Adaptive Cuckoo Search,ACS)算法优化的深度信念网络(Deep Beilef Network,DBN)预测模型ACS-DBN,在给定喷嘴高度、焊接电流、焊接速度、送丝速度这4个工艺参数的基础上预测焊道的熔宽和余高;基于实验法确定最优的隐层层数和隐元数量,构建基于ASC-DBN的WAAM焊道尺寸预测模型;利用仿真实验验证ACS-DBN预测模型的性能,与已有模型对比,结果表明,ACS-DBN模型能有效的映射WAAM焊道尺寸和焊接工艺参数之间的复杂非线关系,控制焊道尺寸的相对误差在6%以内,相对于其他预测模型具有更高的准确性和稳定性。

Welding pass overlap is the essence of wire and arc additive manufacturing(WAAM)technology. Appropriate process parameter selection is of great significance to control the welding pass geometry and improve the dimensional accuracy of the molded parts. A prediction model of deep beilef network(DBN) optimized by adaptive cuckoo search(ACS) algorithm is constructed. The welding width and residual height of the weld pass are predicted based on the four technological parameters of the given nozzle height, welding current, welding speed and wire feeding speed. The optimal number of hidden layers and hidden elements are determined based on the experimental method, and the prediction model of WAAM weld pass size based on ASC-DBN is established. Simulation experiments are used to verify the performance of ASC-DBN prediction model. By comparing with the traditional models, the results show that the ACS-DBN model can effectively map the complex non-linear relationship between the weld pass size and welding process parameters of WAAM, and the prediction error of the weld pass size under the ACS-DBN prediction model is less than 6%, which has higher accuracy and stability compared with other prediction models.