Optimization of process parameters for TC11 alloy via tailoring scanning strategy in laser powder bed fusion

Chang Shu, Zhiyu Zheng, Peiran Lei, Haijie Xu, Xuedao Shu, Khamis Essa

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Front. Mater. Sci. ›› 2024, Vol. 18 ›› Issue (4) : 240710. DOI: 10.1007/s11706-024-0710-z
RESEARCH ARTICLE

Optimization of process parameters for TC11 alloy via tailoring scanning strategy in laser powder bed fusion

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Abstract

TC11, with a nominal composition of Ti–6.5Al–3.5Mo–1.5Zr–0.3Si, is the preferred material for engine blisk due to its high-performance dual-phase titanium alloy, effectively enhancing engine aerodynamic efficiency and service reliability. However, in laser powder bed fusion (L-PBF) of TC11, challenges such as inadequate defect control, inconsistent part quality, and limited optimization of key processing parameters hinder the process reliability and scalability. In this study, computational fluid dynamics (CFD) was used to simulate the L-PBF process, while design of experiments (DoE) was applied to analyze the effect of process parameters and determine the optimal process settings. Laser power was found to have the greatest impact on porosity. The optimal process parameters are 170 W laser power, 1100 mm·s−1 scanning speed, and 0.1 mm hatch spacing. Stripe, line, and chessboard scanning strategies were implemented using the optimal process parameters. The stripe scanning strategy has ~33% (~400 MPa) greater tensile strength over the line scanning strategy and ~12% (~170 MPa) over the chessboard scanning strategy. This research provides technical support for obtaining high-performance TC11 blisks.

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Keywords

laser powder bed fusion / TC11 / parameter optimization / mechanical property / numerical modelling

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Chang Shu, Zhiyu Zheng, Peiran Lei, Haijie Xu, Xuedao Shu, Khamis Essa. Optimization of process parameters for TC11 alloy via tailoring scanning strategy in laser powder bed fusion. Front. Mater. Sci., 2024, 18(4): 240710 https://doi.org/10.1007/s11706-024-0710-z

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Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was funded by “Pioneer” and “Leading Goose” R&D Program of Zhejiang, China (Grant No. 2024C01121) and the University of Birmingham.

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2024 Higher Education Press
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