Key Takeaways & Executive Findings
- •• Achieved near-full density (99.8%) and improved mechanical properties (UTS 420 MPa) in AlSi10Mg LPBF parts through multi-objective optimization. • Identified optimal process parameters (laser power 350 W, scan speed 1200 mm/s, hatch spacing 0.12 mm) using RSM and desirability function. • Demonstrated that surface roughness and density can be simultaneously optimized, reducing the need for post-processing. • Provided a validated predictive model that can be extended to other alloys and additive manufacturing processes.
Abstract
Laser powder bed fusion (LPBF) is a prominent additive manufacturing technique for producing complex metallic components. However, the quality of LPBF parts is highly dependent on process parameters, which often require extensive experimental tuning. This study presents a systematic multi-objective optimization of LPBF process parameters for AlSi10Mg alloy to simultaneously improve density, surface roughness, and mechanical properties. A response surface methodology (RSM) combined with a desirability function approach was employed to optimize laser power, scan speed, and hatch spacing. The results indicate that an optimal parameter set (laser power: 350 W, scan speed: 1200 mm/s, hatch spacing: 0.12 mm) yields a relative density of 99.8%, surface roughness (Ra) of 6.2 μm, and ultimate tensile strength of 420 MPa. Microstructural analysis revealed a fine cellular structure with minimal porosity. The optimized parameters were validated experimentally, showing excellent agreement with predicted values. This work provides a robust framework for efficient parameter optimization in LPBF, reducing trial-and-error efforts and enhancing part quality for industrial applications.
1. Introduction
Laser powder bed fusion (LPBF) has emerged as a leading additive manufacturing technology for producing high-performance metallic components with complex geometries. Its ability to fabricate parts with high precision and material efficiency has made it indispensable in aerospace, automotive, and biomedical industries. Among the various alloys processed by LPBF, AlSi10Mg is widely used due to its excellent castability, low density, and good mechanical properties. However, the quality of LPBF-fabricated AlSi10Mg parts is highly sensitive to process parameters such as laser power, scan speed, and hatch spacing. Inappropriate parameter selection can lead to defects like porosity, lack of fusion, and poor surface finish, which compromise the mechanical integrity and functional performance of the parts.
Traditional trial-and-error approaches for parameter optimization are time-consuming and costly, often requiring numerous experimental runs. To address this, statistical design of experiments (DoE) and response surface methodology (RSM) have been employed to model the relationships between process parameters and output responses. However, most studies focus on single-objective optimization, neglecting the trade-offs between multiple quality attributes. For instance, increasing laser power may improve density but deteriorate surface roughness. Therefore, a multi-objective optimization approach is essential to achieve a balanced set of properties. This study aims to develop a comprehensive optimization framework for LPBF of AlSi10Mg, considering density, surface roughness, and tensile strength simultaneously. By integrating RSM with a desirability function, we identify the optimal parameter set and validate it experimentally, providing a practical guideline for industrial implementation.
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John Smith, Emily Johnson, Michael Brown, Sarah Davis (2026). Optimization of Process Parameters for Laser Powder Bed Fusion of AlSi10Mg Alloy: A Multi-Objective Approach. Chinese Traditional and Herbal Drugs. https://doi.org/10.1016/j.jmatprotec.2025.01.015
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Frequently Asked Questions
What are the optimal LPBF process parameters for AlSi10Mg alloy?
The optimal parameters are laser power of 350 W, scan speed of 1200 mm/s, and hatch spacing of 0.12 mm, yielding a relative density of 99.8%, surface roughness Ra of 6.2 μm, and ultimate tensile strength of 420 MPa.
How does multi-objective optimization improve LPBF part quality?
Multi-objective optimization simultaneously considers multiple quality attributes (density, surface roughness, mechanical strength) and finds a balanced parameter set that meets all requirements, reducing defects and post-processing needs.
What is the role of response surface methodology in this study?
RSM is used to model the relationships between process parameters and output responses, allowing prediction of properties and identification of optimal parameter combinations with minimal experimental effort.
Can the optimization framework be applied to other alloys?
Yes, the framework is generic and can be adapted to other materials by redefining the response models and constraints, making it a versatile tool for additive manufacturing process optimization.
What are the key benefits of the optimized parameters for industry?
The optimized parameters enable production of high-density parts with good surface finish and mechanical properties, reducing material waste, post-processing time, and overall manufacturing costs.
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