High pressure die casting of Fe-based metallic glass

Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams

The use of machine learning techniques to expedite the discovery and development of new materials is an essential step towards the acceleration of a new generation of domain-specific highly functional material systems. In this paper, we use the test case of bulk metallic glasses to highlight the key issues in the field of high throughput predictions and propose a new probabilistic analysis of rules for glass forming ability using rough set theory. This approach has been applied to a broad range of binary alloy compositions in order to predict new metallic glass compositions. Our data driven approach takes into account not only a broad variety of thermodynamic, structural and kinetic based criteria, but also incorporates qualitative and descriptive attributes associated with eutectic points in phase diagrams. For the latter, we demonstrate the use of automated machine learning methods that go far beyond text recognition approaches by also being able to interpret phase diagrams. When combined with structural descriptors, this approach provides the foundations to develop a hierarchical probabilistic predication tool that can rank the feasibility of glass formation.

Near-Net Forming Complex Shaped Zr-Based Bulk Metallic Glasses by High Pressure Die Casting

Forming complex geometries using the casting process is a big challenge for bulk metallic glasses (BMGs), because of a lack of time of the window for shaping under the required high cooling rate. In this work, we open an approach named the “entire process vacuum high pressure die casting” (EPV-HPDC), which delivers the ability to fill die with molten metal in milliseconds, and create solidification under high pressure. Based on this process, various Zr-based BMGs were prepared by using industrial grade raw material. The results indicate that the EPV-HPDC process is feasible to produce a glassy structure for most Zr-based BMGs, with a size of 3 mm × 10 mm and with a high strength. In addition, it has been found that EPV-HPDC process allows complex industrial BMG parts, some of which are hard to be formed by any other metal processes, to be net shaped precisely. The BMG components prepared by the EVP-HPDC process possess the advantages of dimensional accuracy, efficiency, and cost compared with the ones formed by other methods. The EVP-HPDC process paves the way for the large-scale application of BMGs.