# Laser Wire Directed Energy Deposition of 5356 Aluminum Alloy: Process Parameter Optimization and Porosity Prediction

## TL;DR

This paper optimizes laser wire directed energy deposition parameters to reduce porosity in aluminum alloy components using experiments and machine learning.

## Contribution

A novel approach combining experimental analysis and machine learning models to optimize LWDED parameters and predict porosity in 5356 aluminum alloy.

## Key findings

- SVR model achieved best porosity prediction with R2 of 0.8960, RMSE of 0.19, and MAE of 0.15.
- Contour maps and 3D plots visualized porosity variation under parameter interactions.
- Validation experiments showed maximum relative error of 0.514% between model predictions and measurements.

## Abstract

Laser wire directed energy deposition (LWDED) has garnered significant attention for the fabrication of large metallic components. However, the complex coupling effects among its process parameters pose challenges for porosity control. Optimizing parameter combinations to effectively minimize porosity is therefore critical to the broader adoption of this technology. In this study, systematic experiments and modeling were conducted to optimize the LWDED process parameters and predict porosity. First, single-factor and orthogonal experiments were performed to evaluate the individual effects of laser power, scanning speed, wire feeding speed, and air pressure on porosity. Subsequently, range analysis and analysis of variance were employed to determine the influence of each parameter and the significance of their interactions. Four machine learning models—SVR, RF, GPR, and XGBoost—were then trained and compared. Among them, the SVR model exhibited the best predictive performance, achieving an R2 of 0.8960, an RMSE of 0.19, and an MAE of 0.15, outperforming the other three models. Based on this, the SVR model was further utilized to establish the mapping between process parameters and porosity. Contour maps and three-dimensional surface plots were generated to visualize porosity variation patterns under interacting parameters. Validation experiments showed that the maximum relative error between model predictions and experimental measurements was 0.514%, with an average error of 0.251%. This study provides a reliable reference for selecting low-porosity parameter combinations in the LWDED fabrication of 5356 aluminum alloy components.