Optimization of Process Parameters for Laser Powder Bed Fusion of Ti-6Al-4V Alloy: A Multi-Objective Approach

Abstract

Laser powder bed fusion (LPBF) is a promising additive manufacturing technique for producing complex Ti-6Al-4V components with high strength-to-weight ratios. However, the quality of printed parts is highly sensitive to process parameters, which often require extensive experimental tuning. This study presents a systematic multi-objective optimization of LPBF process parameters—laser power, scan speed, hatch spacing, and layer thickness—to simultaneously minimize porosity and surface roughness while maximizing relative density and microhardness. A response surface methodology (RSM) with a central composite design (CCD) was employed to develop predictive models, and a desirability function approach was used to find the optimal parameter set. The optimized parameters were validated experimentally, achieving a relative density of 99.8%, a surface roughness (Ra) of 4.2 μm, and a microhardness of 410 HV, representing a significant improvement over baseline conditions. Microstructural analysis revealed a refined α' martensitic structure with reduced porosity. The results demonstrate that the proposed optimization framework can effectively enhance the quality of LPBF-produced Ti-6Al-4V parts, offering a robust methodology for process parameter optimization in additive manufacturing.

1. Introduction

Additive manufacturing (AM) has revolutionized the production of complex metallic components, particularly in aerospace and biomedical industries, where materials like Ti-6Al-4V are favored for their excellent strength-to-weight ratio and corrosion resistance. Among AM techniques, laser powder bed fusion (LPBF) stands out for its ability to fabricate intricate geometries with high dimensional accuracy. However, the quality of LPBF parts is critically dependent on a multitude of process parameters, including laser power, scan speed, hatch spacing, and layer thickness. These parameters directly influence the melt pool dynamics, solidification behavior, and thermal gradients, which in turn affect porosity, surface roughness, and mechanical properties. Consequently, optimizing these parameters is essential to produce parts with consistent quality and performance.

Traditional trial-and-error approaches are time-consuming and often fail to capture the complex interactions between parameters. To address this, statistical design of experiments (DoE) methods, such as response surface methodology (RSM), have been widely adopted to model and optimize AM processes. RSM enables the development of predictive models that relate process parameters to output responses, allowing for the identification of optimal conditions. However, most existing studies focus on single-objective optimization, neglecting the trade-offs between multiple quality attributes. In practice, achieving high density may come at the expense of surface finish, and vice versa. Therefore, a multi-objective optimization approach is necessary to balance these conflicting goals.

This study aims to fill this gap by employing a multi-objective optimization framework for LPBF of Ti-6Al-4V. Using a central composite design (CCD) and desirability function analysis, we simultaneously optimize four key responses: relative density, surface roughness, microhardness, and porosity. The optimal parameter set is validated experimentally, and the resulting microstructure is characterized. The findings provide a robust methodology for process optimization in LPBF, with significant implications for industrial adoption.