Bcc matrix phase is for toughening at ambient temperatures, and T2 phase is for strengthening and also for oxidation resistance. However, the oxidation resistance of T2 phase is still under investigation. B2-NiAl phase has been utilized as coating materials for Ni-based superalloys for many years...
Real-time monitoring and early warning of human health conditions is an important function of wearable devices. Along with the development of the Internet of Things and the medical drive for early detection and treatment, wearable devices will become inc
Ti, Ni) form the basis of fielded high-temperature alloys. For instance, a titanum alloy was employed in hot aeroshell structures in the SR-715,15, the nose section of the X-43 contained a SD 180 tungsten heavy alloy50, a Haynes Ni-base alloy was used in the Mach 7 X-43A variant51...
Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this ...
<div p-id="p-0001">Formed alloy strips including zirconium alloy strips that demonstrate improved formability are disclosed. The strips of the present disclosure have a purity and crystalline microstr
The composition of the 9 Cr-1 Mo steel is comprised of at least Chromium (Cr), Molybdenum (Mo), Carbon (C), Titanium (Ti), and potentially additional elements, with the balance Iron (Fe) and other impurities. The composition is preferably restricted to a particular one for the following...
The invention relates to extruded products suitable for turning, made from aluminium alloy with a composition (in weight %) of: 0.4 - 0.8 Si; 0.8 - 1.2 Mg; 0.20 - 0.4 Cu; 0.05 - 0.4 Fe; Mn ≤ 0.10; Ti < 0.15; Cr ≤ 0.10; Bi ≤ 0.8; Pb ≤ 0.4; other elements < 0.05 each ...
High strength aluminum alloys and high strength aluminum alloy castings The present invention relates to high strength aluminum alloys2.0 to 13.0% by weight of copper (Cu)0.4-4.0 wt% manganese (MN)0.4 to 2.0 weight% iron (FE)6.0-10.0% by weight silicon (SI)Excess of 7.0 or less percent ...
b The measured vs predicted ln(Tc) of various superconductors based on a random forest model presented in ref. 91. The same model can predict Tc of several distinct superconducting classes (blue markers: low-Tc materials; green markers: iron-based superconductors; red markers: cuprate ...
Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since exten