Open Access Articles

[SAMPLE] Enhancing the Mechanical Performance of Additively Manufactured Ti-6Al-4V Lattices

A Data-Driven Study on Process Parameters and Microstructure

by Ada Sample, Bruno Demo

International Journal of Materials Innovation and Engineering, Vol. 1 No. 1 (2026), 1-15

DOI: https://doi.org/10.00000/ijmie.v1i1.1

Submission received: 2026-05-16 / Revised: 2026-08-06 / Accepted: 2026-08-24 / Published: 2026-09-01

How to Cite

[SAMPLE] Enhancing the Mechanical Performance of Additively Manufactured Ti-6Al-4V Lattices: A Data-Driven Study on Process Parameters and Microstructure. (2026). International Journal of Materials Innovation and Engineering, 1(1), 1-15. https://doi.org/10.00000/ijmie.v1i1.1

Abstract

This is a sample article created to preview the IJMIE article page layout; it contains no real research data and can be safely deleted before launch.

Additively manufactured titanium alloy (Ti-6Al-4V) lattice structures offer exceptional specific strength for aerospace and biomedical applications, yet their mechanical performance is highly sensitive to laser powder-bed fusion process parameters. In this study we systematically vary laser power, scan speed and hatch spacing to map their influence on relative density, microstructure and quasi-static compressive response. Using a design-of-experiments approach combined with machine-learning surrogate models, we identify a processing window that increases yield strength by up to 18% while maintaining a relative density above 99.2%. Electron backscatter diffraction reveals a transition from columnar prior-β grains to a refined basketweave α′ martensite that correlates strongly with the improved strength. The results demonstrate that data-driven process optimisation can reliably tailor the strength-to-weight ratio of metal lattices, and provide design guidelines transferable to other alloy systems.

Keywords:

additive manufacturing, titanium alloys (Ti-6Al-4V), lattice structures, laser powder-bed fusion, machine learning, mechanical properties

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References

Sample, A., & Demo, B. (2025). Process-structure-property relationships in additively manufactured titanium lattices. Journal of Sample Materials, 12(3), 210-225.

Placeholder, C. D. (2024). Machine learning for laser powder-bed fusion parameter optimisation. Additive Manufacturing Reviews, 8(1), 45-67.

Example, E. F., & Test, G. H. (2023). Microstructural evolution of Ti-6Al-4V under varying scan strategies. Materials Science Sample Letters, 5, 1001-1012.

Demo, B., Sample, A., & Placeholder, C. (2022). Mechanical characterisation of open-cell metal lattices: a review. Review of Sample Engineering, 30, 88-140.