Programmable mechanical properties of additively manufactured novel steel
Source abstract
Abstract
Abstract Tailoring thermal history during additive manufacturing (AM) offers a feasible approach to customise the microstructure and properties of materials without changing alloy compositions or post-heat treatment, which is generally overlooked as it is hard to achieve in commercial materials. Herein, a customised Fe–Ni–Ti–Al maraging steel with rapid precipitation kinetics offers the opportunity to leverage thermal history during AM for achieving large-range tunable strength-ductility combinations. The Fe–Ni–Ti–Al steel was processed by laser-directed energy deposition (LDED) with different deposition strategies to tailor the thermal history. As the phase transformation and in-situ formation of multi-scale secondary phases of the Fe–Ni–Ti–Al steel are sensitive to the thermal histories, the deposited steel achieved a large range of tuneable mechanical properties. Specifically, the interlayer paused deposited sample exhibits superior tensile strength (∼1.54 GPa) and moderate elongation (∼8.1%), which is attributed to the formation of unique hierarchical structures and the in-situ precipitation of high-density η-Ni3(Ti, Al) during LDED. In contrast, the substrate heating deposited sample has an excellent elongation of 19.3% together with a high tensile strength of 1.24 GPa. The achievable mechanical property range via tailoring thermal history in the LDED-built Fe–Ni–Ti–Al steel is significantly larger than most commercial materials. The findings highlight the material customisation along with AM’s unique thermal history to achieve versatile mechanical performances of deposited materials, which could inspire more property or function manipulations of materials by AM process control or innovation.
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Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
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