High strength and ductility in modern metals manufactured by additive manufacturing opens new possibilities for components in many industries including aerospace and medicine. The current alloys which meet these strict performance criteria are costly and difficult to make.
A new approach for creating high-performance alloys can dramatically increase their cost-effectiveness, as well as the range of uses they are suited to. A team of Georgia Tech researchers and University of Massachusetts Amherst researchers have developed an alloy with a high-entropy dual-phase structure that has superior strength and ductility to existing AM alloys.
The new material, LTNC-CoCrMo was created using a Laser Powder Bed Fusion (LPBF). The LTNC treatment improves the mechanical properties of the metal by homogenizing its microstructure and texture, eliminating residual stress, and reducing its anisotropy. The LTNC also reduces an alloy's susceptibility for cracking and fatigue when processing and increases low-temperature ductility.
The LTNC treatment not only reduced the alloy's tensile strength and yield strength, but also increased its ductility to about 40% compared to Alloy 22, which was untreated. The increase in ductility is attributed to the formation of nitrogen and carbon atoms in the alloy's matrix. The XRD diffractogram shows sharp peaks on the surface.
Addition of carbon to aluminum alloys increases their plasticity. But the amount necessary to achieve the increase is minimal--less that 0.1% weight. The LTNC treatment increases the amount of activated carbon that can be added to an aluminum-based alloy while maintaining its high hardness and strength. The LTNC CoCrMo-treated LTNC CoCrMo is 12% more activated than the 10% possible with conventional metallurgical means.
GKN Additive Materials has been working with industrial partners on the development of a new low alloy steel powder class for additive manufacturing. These powders are chemically tailored in order to meet each partner's unique mechanical requirements. The company has created a wide range of powders that can be used in both gas and water atomization processes. These powders contain varying amounts of silicon, manganese, molybdenum, nickel and chromium.
This project was supported by a Laboratory Directed Research and Development award from Sandia National Laboratories, and a Department of Energy Integrated Computational Materials Engineering (ICME) program. ICME model is used to determine the ideal composition of an alloy by minimizing its variability and maximizing it's likelihood of success for an additive manufacture build. This reduces reliance and accelerates materials development. Ames Lab scientists have contributed to this theory. To calculate model parameters, the Hall-Petch formula was used. Machine learning was used to create surrogate model that were then evaluated using statistical analysis tools like Spearman's ranking correlation and Sobol indices. On reasonable request, the data sets used in this research are available.
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