A method of forming an article includes producing a base powder including a plurality of base particles. Each base particle includes an external surface and a first material. The method further includes removing one or more oxides from the external surface of each base particle to form a cleaned powder including a plurality of cleaned particles. Each cleaned particle includes a cleaned external surface made of the first material. The method further includes coating the cleaned external surface of each cleaned particle with a second material having a greater oxidation resistance than the first material to form a coated powder including a plurality of coated particles. Each coated particle includes an external layer including the second material that fully covers the cleaned external surface made of the first material. The method further includes forming the article using the coated powder.

A machine learning method includes: calculating a reward for a result of decision of a cold isostatic pressing process condition based on an acquired state variable; updating, based on the reward, a function to decide at least one cold isostatic pressing process condition from the state variable; and deciding a cold isostatic pressing process condition which yields a highest reward, by repeating update of the function. The cold isostatic pressing process condition is at least one of a first parameter related to an object to be processed, a second parameter related to a preceding process of a cold isostatic pressing process, and a third parameter related to operating conditions of a cold isostatic pressing apparatus, and the at least one physical amount is related to at least one of sterilization and inactivation, shucking, improvement of taste and flavor, and improvement of texture and nourishment of the object to be processed.

A machine learning method includes: calculating a reward for a result of decision of a cold isostatic pressing process condition based on an acquired state variable; updating, based on the reward, a function to decide at least one cold isostatic pressing process condition from the state variable; and deciding a cold isostatic pressing process condition which yields a highest reward, by repeating update of the function. The cold isostatic pressing process condition is at least one of a first parameter related to an object to be processed, a second parameter related to a preceding process of a cold isostatic pressing process, and a third parameter related to operating conditions of a cold isostatic pressing apparatus, and the at least one physical amount is related to at least one of sterilization and inactivation, shucking, improvement of taste and flavor, and improvement of texture and nourishment of the object to be processed.

A machine learning method includes: calculating a reward for a result of decision of a cold isostatic pressing process condition based on an acquired state variable; updating, based on the reward, a function to decide at least one cold isostatic pressing process condition from the state variable; and deciding a cold isostatic pressing process condition which yields a highest reward, by repeating update of the function. The cold isostatic pressing process condition is at least one of a first parameter related to an object to be processed, a second parameter related to a preceding process of a cold isostatic pressing process, and a third parameter related to operating conditions of a cold isostatic pressing apparatus, and the at least one physical amount is related to at least one of sterilization and inactivation, shucking, improvement of taste and flavor, and improvement of texture and nourishment of the object to be processed.

A structured multi-phase composite which include a metal phase, and a low stiffness, high thermal conductivity phase or encapsulated phase change material, that are arranged to create a composite having high thermal conductivity, having reduced/controlled stiffness, and a low CTE to reduce thermal stresses in the composite when exposed to cyclic thermal loads. The structured multi-phase composite is useful for use in structures such as, but not limited to, high speed engine ducts, exhaust-impinged structures, heat exchangers, electrical boxes, heat sinks, and heat spreaders.

The present invention involves a graphene-containing rare earth permanent magnet material and preparation method thereof. The graphene-containing rare earth permanent magnet material, comprising: 20.6 to 23.4 weight percent of neodymium, 6.6 to 7.5 weight percent of praseodymium, 0.95 to 1.20 weight percent of boron, 0.4 to 0.6 weight percent of cobalt, 0.11 to 0.15 weight percent of copper, 2.0 to 2.4 weight percent of lanthanum, 1.7 to 2.1 weight percent of cerium, 1 to 5 weight percent of graphene, a remainder being iron. The graphene-containing rare earth permanent magnet material exhibits excellent temperature resistance, good conductivity and magnet properties even without any heavy rare earth elements like terbium or dysprosium, which dramatically reduces the cost, promotes the efficient utilization of rare earth resources and improves product quality. The preparation method within this invention is simple to realize, easy to control, cost-effective and has high production efficiency and stable product performances.

The present invention involves a graphene-containing rare earth permanent magnet material and preparation method thereof. The graphene-containing rare earth permanent magnet material, comprising: 20.6 to 23.4 weight percent of neodymium, 6.6 to 7.5 weight percent of praseodymium, 0.95 to 1.20 weight percent of boron, 0.4 to 0.6 weight percent of cobalt, 0.11 to 0.15 weight percent of copper, 2.0 to 2.4 weight percent of lanthanum, 1.7 to 2.1 weight percent of cerium, 1 to 5 weight percent of graphene, a remainder being iron. The graphene-containing rare earth permanent magnet material exhibits excellent temperature resistance, good conductivity and magnet properties even without any heavy rare earth elements like terbium or dysprosium, which dramatically reduces the cost, promotes the efficient utilization of rare earth resources and improves product quality. The preparation method within this invention is simple to realize, easy to control, cost-effective and has high production efficiency and stable product performances.

A method of titanium rod additive manufacturing may comprise: mixing a plurality of powdered metals comprising titanium, iron, vanadium, and aluminum to produce a powder blend; isostatic pressing the powder blend to form a billet having a cross-sectional profile; cutting the billet to form a rod feedstock having the first cross-sectional profile; loading the rod feedstock into an additive manufacturing machine configured to deposit the rod feedstock; and producing a metallic component from the rod feedstock.

A method of titanium rod additive manufacturing may comprise: mixing a plurality of powdered metals comprising titanium, iron, vanadium, and aluminum to produce a powder blend; isostatic pressing the powder blend to form a billet having a cross-sectional profile; cutting the billet to form a rod feedstock having the first cross-sectional profile; loading the rod feedstock into an additive manufacturing machine configured to deposit the rod feedstock; and producing a metallic component from the rod feedstock.

The invention relates to a process for fabricating complex mechanical shapes from metal or ceramic, and in particular to fabricating complex mechanical shapes using a pressure-assisted sintering technique to address problems relating to variations in specimen thickness and tooling, or densification gradients, by 3-D printing of a sacrificial, self-destructing powder mold is created using e.g. alumina and swellable binders such as polysaccharides. The binder-free sintering powder that forms the manufactured item is injected into the mold, and high pressure is applied. The powder assembly can then be sintered by any pressure assisted technique to full densification and the self-destructing mold allows the release of the fully densified complex manufactured item.