Results 211 to 220 of about 1,429,804 (373)
An Operational Matrix of Fractional Differentiation of the Second Kind of Chebyshev Polynomial for Solving Multiterm Variable Order Fractional Differential Equation [PDF]
Jianping Liu, Xia Li, Limeng Wu
openalex +1 more source
Jacobi matrix differential equation, polynomial solutions, and their properties
E. Defez, L. Jódar, A. Law
semanticscholar +1 more source
High‐performance nickel‐based superalloys are often not processible in additive manufacturing (AM) due to hot cracking. The findings in this manuscript propose an efficient method to mitigate cracking and enhance mechanical properties of these alloys by producing a metal matrix composite, contributing to the material and process perspective of the AM ...
Klaus Büßenschütt +3 more
wiley +1 more source
The fused filament‐fabricated MAR‐M247 alloy without hot isostatic pressing shows the lowest porosity of 4%. Heat treatment at 1220 °C produces coarse precipitates and carbides. Specimens heat‐treated at 1220 °C exhibit higher tensile strength (683 MPa) and elongation (10%) at room temperature.
Haneen Daoud +7 more
wiley +1 more source
This work reveals the phase composition and quantitative morphology analysis of precipitation‐hardened Fe32Cu12Ni11Ti16Al29 complex‐concentrated alloy. The precipitates are shown to have a high coherency. Morphology transition between sphere, cuboidal, and elongated morphology is observed. Finally, the overaging behavior is captured using microhardness.
Rostyslav Nizinkovskyi +4 more
wiley +1 more source
A novel method for approximate solution of two point non local fractional order coupled boundary value problems. [PDF]
Tadoummant L +4 more
europepmc +1 more source
Cubic Bézier curves are used in the synthesis of novel surface‐based metamaterials with tunable mechanical properties. Surface‐based geometries are 3D printed and tested in compression. The resulting mechanical properties are correlated to changes in the shape of the base curve, with high potential in energy absorption through the adjustment of their ...
Alberto Álvarez‐Trejo +2 more
wiley +1 more source
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations. [PDF]
Chen C, Yang Y, Xiang Y, Hao W.
europepmc +1 more source

