Optimization of Ni-Cu binder composition and sintering time for enhanced mechanical properties of Mo alloys via liquid phase sintering
Source abstract
Abstract
Liquid phase sintering of refractory alloys offers a promising approach to enhance mechanical properties while ensuring cost-effectiveness and near-net shaping capabilities. This study systematically explores the impact of sintering time and Ni-Cu binder composition on densification, shape retention, and mechanical properties of Mo alloys processed via liquid phase sintering. The aim is to identify optimal binder composition and sintering time for low-cost production of dense, near-net-shape Mo alloys with superior mechanical properties. The sinterability of Mo compacts is enhanced as the Ni-Cu ratio increases. However, excessive Ni content induces geometric distortion during sintering. Alloys with Ni-rich binders exhibit two liquids at the sintering temperature, each with differing Mo solubility, resulting in a dual-phase matrix on solidification. Both Cu-rich and Ni-rich binder compositions exhibit brittle δ-MoNi intermetallic compound formation, whose quantity increases with prolonged sintering times. Alloys characterized by low Mo solubility in the matrix, high Mo volume fraction, high Mo grain contiguity, and large dihedral angles demonstrate high structural integrity and resistance to geometric distortion. Conversely, alloys featuring high matrix volume fraction, low grain contiguity, elevated Mo solubility in the binder, and strong Mo-matrix interfaces exhibit enhanced strength, hardness, and ductility. Notably, an alloy with a Ni-Cu ratio of 3:1 sintered for just 30 min at 1420°C demonstrates outstanding mechanical properties—hardness of 410 HV, tensile strength of 920 MPa, and elongation of 9 %—comparable to or better than many previously reported refractory alloys.
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Research context
OpenAlex topics: Advanced materials and composites · Aluminum Alloys Composites Properties · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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