Multi-layer multi-pass friction rolling additive manufacturing of Al alloy: Toward complex large-scale high-performance components
Source abstract
Abstract
At present, the emerging solid-phase friction-based additive manufacturing technology, including friction rolling additive manufacturing (FRAM), can only manufacture simple single-pass components. In this study, multi-layer multi-pass FRAM-deposited aluminum alloy samples were successfully prepared using a non-shoulder tool head. The material flow behavior and microstructure of the overlapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys. The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adjacent passes in the overlapped center area. Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone, which made the recrystallization degree in the left and right edge zones of the overlapped zone the highest, followed by the overlapped center zone and the non-overlapped zone. The tensile strength of the overlapped zone exceeded 90% of that of the single-pass deposition sample. It is proved that although there are uneven grooves on the surface of the overlapping area during multi-layer and multi-pass deposition, they can be filled by the flow of materials during the deposition of the next layer, thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area. The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Aluminum Alloys Composites Properties · Advanced Welding Techniques Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
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