Lightweight alloy design for laser-based additive manufacturing

Abstract

Lightweight metals are key to reducing energy consumption and enhancing the performance of advanced structures in aerospace, automotive, and related industries. This thesis investigates three complementary strategies for developing high-performance lightweight alloys underpinned by laser-based processing and computational materials design.

First, a novel laser welding-brazing approach was applied to an immiscible material system (Mg/steel), which traditionally suffers from weak metallurgical bonding. By developing thermodynamic models to guide the selection of intermediate elements, robust joints were achieved, thus enabling reliable multi-material structures. The obtained joint strength is the highest compared to all previously reported literature.

Second, a composition-optimisation model based on precipitation growth kinetics was developed to design ultra-high-strength aluminium alloys for laser powder bed fusion (LPBF). Through direct post-processing (ageing), the as-printed alloys reached yield strengths up to 650 MPa—among the highest reported to date for printable Al alloys—by capitalising on a finely tuned nanoscale precipitate structure that suppresses hot cracking and refines grain morphology.

Finally, a genetic algorithm-based framework was employed to design Ti-based high-entropy alloys (HEAs) featuring transformation-induced plasticity (TRIP) effects. This approach systematically integrated phase stability and manufacturability criteria to identify compositions with exceptional strength–ductility synergy, suitable for laser-based additive manufacturing.

Overall, this thesis highlights how integrated computational–experimental strategies and advanced laser technologies can unlock unprecedented mechanical performance in lightweight alloys. The findings pave the way for next-generation structural materials, facilitating more efficient and sustainable engineering applications.

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Identifiers

ORCID for Chengwei Zang: orcid.org/0009-0004-6609-1598

ORCID for Pedro Rivera: orcid.org/0000-0002-0419-8347

ORCID for Dikai Guan: orcid.org/0000-0002-3953-2878

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