Abstract: A method for fabricating a physical object is disclosed, including receiving a 3D representation of the physical object, modifying the 3d representation of the physical object, determining a fabrication strategy for forming the physical object, generating fabrication instructions for formation of the physical object based upon the step of determining the fabrication strategy, and fabricating the physical object based upon the step of generating fabrication instructions. The method and system is configured to modify a three-dimensional (3D) representation of a physical object to, in part, compensate for material shrinkage and material removal, improve surface finish, and decrease feature damage during fabrication of the physical object.

Abstract: An additive manufacturing system includes an electrode head comprising an array of electrodes for depositing material to form a three-dimensional part. The array includes a first plurality of electrodes formed from a first metallic material having a first ductility and a first hardness, and a second plurality of electrodes formed from a second metallic material having a second ductility and a second hardness, wherein the first ductility is greater than the second ductility and the second hardness is greater than the first hardness. A power source provides electrical power for establishing a welding arc for each electrode. A drive roll system drives each electrode.

Abstract: The disclosed technology generally relates to consumable electrode wires and more particularly to consumable electrode wires having a core-shell structure, where the core comprises aluminum. In one aspect, a welding wire comprises a sheath having a steel composition and a core surrounded by the sheath. The core comprises aluminum (Al) at a concentration between about 3 weight % and about 20 weight % on the basis of the total weight of the welding wire, where Al is in an elemental form or is alloyed with a different metal element. The disclosed technology also relates to welding methods and systems adapted for using the aluminum-comprising electrode wires.

Abstract: An additive manufacturing system includes an electrode head comprising an array of electrodes for depositing material to form a three-dimensional attachment structure connecting first and second prefabricated metallic parts. The array includes a first plurality of electrodes formed from a first metallic material having a first ductility and a first hardness, and a second plurality of electrodes formed from a second metallic material having a second ductility and a second hardness, wherein the first ductility is greater than the second ductility and the second hardness is greater than the first hardness. A power source provides power for heating each electrode. A drive roll system drives each electrode.

Abstract: Embodiments of systems and methods for characterizing weldments are disclosed. One embodiment includes a method of generating an algorithm for classifying weldments as meeting or not meeting a specification. Training data is read by a machine learning system. The training data includes cross-sectional images of training weldments, truth data indicating whether the training weldments meet the specification or not, and training weld data associated with creating the training weldments. The machine learning system trains up an algorithm using the training data such that the resultant algorithm can classify a subsequent test weldment as meeting the specification or not meeting the specification when a cross-sectional image of the test weldment and test weld data used to create the test weldment are read and processed by the classification algorithm as trained.

Abstract: An additive manufacturing system includes an electrode head comprising an array of electrodes for depositing material to form a three-dimensional attachment structure connecting first and second prefabricated metallic parts. The array includes a first plurality of electrodes formed from a first metallic material having a first ductility and a first hardness, and a second plurality of electrodes formed from a second metallic material having a second ductility and a second hardness, wherein the first ductility is greater than the second ductility and the second hardness is greater than the first hardness. A power source provides power for heating each electrode. A drive roll system drives each electrode.

Abstract: An additive manufacturing system includes an electrode head comprising an array of electrodes for depositing material to form a three-dimensional part. The array includes a first plurality of electrodes formed from a first metallic material having a first ductility and a first hardness, and a second plurality of electrodes formed from a second metallic material having a second ductility and a second hardness, wherein the first ductility is greater than the second ductility and the second hardness is greater than the first hardness. A power source provides electrical power for establishing a welding arc for each electrode. A drive roll system drives each electrode.

Abstract: The disclosed technology generally relates to consumable electrode wires and more particularly to consumable electrode wires having a core-shell structure, where the core comprises aluminum. In one aspect, a welding wire comprises a sheath having a steel composition and a core surrounded by the sheath. The core comprises aluminum (Al) at a concentration between about 3 weight % and about 20 weight % on the basis of the total weight of the welding wire, where Al is in an elemental form or is alloyed with a different metal element. The disclosed technology also relates to welding methods and systems adapted for using the aluminum-comprising electrode wires.

Abstract: Embodiments of systems and methods for characterizing weldments are disclosed. One embodiment includes a method of generating an algorithm for classifying weldments as meeting or not meeting a specification. Training data is read by a machine learning system. The training data includes cross-sectional images of training weldments, truth data indicating whether the training weldments meet the specification or not, and training weld data associated with creating the training weldments. The machine learning system trains up an algorithm using the training data such that the resultant algorithm can classify a subsequent test weldment as meeting the specification or not meeting the specification when a cross-sectional image of the test weldment and test weld data used to create the test weldment are read and processed by the classification algorithm as trained.