PDF(Pubmed)
Abstract：
Laser-arc hybrid additive manufacturing (LAHAM) holds substantial potential in industrial applications, yet ensuring dimensional accuracy remains a major challenge. Accurate prediction and effective control of the geometrical dimensions of the deposited layers are crucial for achieving this accuracy. The width and height of the deposited layers, key indicators of geometric dimensions, directly affect the forming precision. This study conducted experiments and in-depth analysis to investigate the influence of various process parameters on these dimensions and proposed a predictive model for accurate forecasting. It was found that the width of the deposited layers was positively correlated with laser power and arc current and negatively correlated with scanning speed, while the height was negatively correlated with laser power and scanning speed and positively with arc current. Quantitative analysis using the Taguchi method revealed that the arc current had the most significant impact on the dimensions of the deposited layers, followed by scanning speed, with laser power having the least effect. A predictive model based on extreme gradient boosting (XGBoost) was developed and optimized using particle swarm optimization (PSO) for tuning the number of leaf nodes, learning rate, and regularization coefficients, resulting in the PSO-XGBoost model. Compared to models enhanced with PSO-optimized support vector regression (SVR) and XGBoost, the PSO-XGBoost model exhibited higher accuracy, the smallest relative error, and performed better in terms of Mean Relative Error (MRE), Mean Square Error (MSE), and Coefficient of Determination R2 metrics. The high predictive accuracy and minimal error variability of the PSO-XGBoost model demonstrate its effectiveness in capturing the complex nonlinear relationships between process parameters and layer dimensions. This study provides valuable insights for controlling the geometric dimensions of the deposited layers in LAHAM.
摘要：
激光-电弧混合增材制造（LAHAM）在工业应用中具有巨大潜力，然而，确保尺寸精度仍然是一个重大挑战。准确预测和有效控制沉积层的几何尺寸对于实现这种精度至关重要。沉积层的宽度和高度，几何尺寸的关键指标，直接影响成形精度。本研究进行了实验和深入分析，以研究各种工艺参数对这些维度的影响，并提出了用于准确预测的预测模型。发现沉积层的宽度与激光功率和电弧电流呈正相关，与扫描速度呈负相关。高度与激光功率和扫描速度呈负相关，与电弧电流呈正相关。使用Taguchi方法的定量分析表明，电弧电流对沉积层的尺寸影响最大，其次是扫描速度，激光功率影响最小。开发了基于极端梯度提升(XGBoost)的预测模型,并使用粒子群优化(PSO)优化了叶节点数,学习率，和正则化系数，得到PSO-XGBoost模型。与PSO优化的支持向量回归(SVR)和XGBoost增强的模型相比，PSO-XGBoost模型表现出更高的精度，最小的相对误差，在平均相对误差(MRE)方面表现更好，均方误差(MSE)，和确定系数R2度量。PSO-XGBoost模型的高预测精度和最小的误差可变性证明了其在捕获过程参数和层尺寸之间的复杂非线性关系方面的有效性。这项研究为控制LAHAM中沉积层的几何尺寸提供了有价值的见解。
