多层多道次LDED工艺参数智能决策与多目标预测

Intelligent Parameter Decision-Making and Multi-objective Prediction for Multi-layer and Multi-pass LDED Process

摘要:

表征多层多道次激光金属打印件形貌质量的主要参数是表面粗糙度和实际打印高度与理论模型高度之间的误差。采用田口法建立工艺参数组合与金属打印形貌质量（高度误差和粗糙度）多目标表征之间的关联性。先通过信噪比和灰色关联分析法预测多层多道次打印的最优参数组合为：激光功率800 W、送粉速率0.3 r/min、步距1.6 mm、扫描速度20 mm/s；随后构建遗传贝叶斯-反向传播（GB-BP）网络对多目标响应进行预测。与传统反向传播网络相比，GB-BP网络对高度误差和表面粗糙度的预测精度分别提高了43.14%和71.43%。该网络可以准确预测多层多道次激光定向能量沉积金属打印部件的形貌和质量的多目标表征。

Abstract:

The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height. The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology (height error and roughness). Results show that using the signal-to-noise ratio and grey relational analysis, the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows: laser power of 800 W, powder feeding rate of 0.3 r/min, step distance of 1.6 mm, and scanning speed of 20 mm/s. Subsequently, a Genetic Bayesian-back propagation (GB-BP) network is constructed to predict multi-objective responses. Compared with the traditional back propagation network, the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14% and 71.43%, respectively. This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.