Improvement of Ductility of Powder Metallurgy Titanium Alloys by Addition of Rare Earth Element

Ti-4.5Al-6.0Mo-1.5Fe, Ti-6Al-1Mo-1Fe and Ti-6Al-4V alloys were prepared by blended elemental powder metallurgy （PM） process, and the effects of Nd on the microstructures and mechanical properties were investigated by scanning electron microscopy （SEM）, transmission electron microscopy （TEM） and X-ray diffraction （XRD）. It was found out that the addition of Nd increased the density of sintered titanium alloys slightly by a maximum increment of 1% because small amount of liquid phase occurred during sintering. The addition of Nd shows little effect on the improvement of tensile strength, while the elongation is significantly improved. For example, the elongation of Ti-4.SAl-6.0Mo-1.5Fe can be increased from 1% without addition of Nd to 13% at a Nd content of 1.2 wt pct.

Preparation of TiAl alloy powder by high-energy ball milling and diffusion reaction at low temperature

In this paper, TiAl alloy powders were prepared successfully by high-energy ball milling and diffusion reaction in vacuum at low temperature. The titanium powder, aluminum powder, and titanium hydride powder were used as raw materials. The samples were characterized by scanning electron microscopy（SEM）, X-ray diffraction（XRD）, field-emission scanning electron microscopy（FESEM）, and differential thermal analysis（DTA）. The results show that the alloy powders with the main intermetallic compounds of TiAl are obtained using Ti-Al powders and TiH2-Al powders after heated for 2 h at 500 ℃,3 h at 600 ℃,and 3 h at 750 ℃,respectively.The average grain sizes of alloy powder are about 45 and20 μm with irregular shape, respectively. The prepared TiAl alloy powders are relatively pure, and the average quality content of oxygen in the alloy powders is0.33 wt%. The forming process of alloy powder contains both the diffusion reaction of Ti and Al,which gives priority to the diffusion reaction of aluminum.

基于Logistic回归模型的TC4零件激光熔化沉积工艺参数分析（英文）

In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cladding layer or that between cladding layer and the substrate with optimal process parameters, the probability of internal defects of parts can be reduced, and the mechanical properties of parts will be greatly improved. To address the problem that whether the part made by LMD has internal defects, in this paper we designed the orthogonal rotation experiments through selecting different process parameters. Then a Logistic Regression model was built based on the experiments data. The calculation result of the regression model was in good agreement with the result of authentication test. Therefore, this Logistic Regression model has important reference for selecting LMD process parameters.

Optimized Filling of a Given Cuboid with Spherical Powders for Additive Manufacturing

In additive manufacturing(also known as 3D printing),a layer-by-layer buildup process is used for manufacturing parts.Modern laser 3D printers can work with various materials including metal powders.In particular,mixing various-sized spherical powders of titanium alloys is considered most promising for the aerospace industry.To achieve desired mechanical properties of the final product,it is necessary to maintain a certain proportional ratio between different powder fractions.In this paper,a modeling approach for filling up a rectangular 3D volume by unequal spheres in a layer-by-layer manner is proposed.A relative number of spheres of a given radius(relative frequency)are known and have to be fulfilled in the final packing.A fast heuristic has been developed to solve this special packing problem.Numerical results are compared with experimental findings for titanium alloy spherical powders.The relative frequencies obtained by using the imposed algorithm are very close to those obtained by the experiment.This provides an opportunity for using a cheap numerical modeling instead of expensive experimental study.