Abstract: A friction stir additive welding screw is provided. In one aspect, the friction stir additive welding screw includes a first portion configured to be coupled to a friction stir additive welding device, and a second portion configured to penetrate a work-piece. The second portion includes a plurality of large threads, each large thread extending in a generally longitudinal direction. The second portion also includes a plurality of fine threads positioned along an edge of each large thread, and a plurality of teeth at a tip of second portion.

Abstract: A friction stir additive welding device configured to join a first work-piece and second work-piece is provided. In one aspect, the device includes a nosecone having a sidewall extending from a first end to a second end, a channel extending from the first end to the second end along a longitudinal axis of the nosecone, and a screw positioned within the channel. The first end is configured to couple the nosecone to a robotic arm and the second end has a surface configured to be advanced along a weld line between the first and second work-pieces. A central axis of the screw defines an angle that is less than 90 degrees relative to a plane that is perpendicular to surfaces of the first and second work-pieces as the surface of the nosecone advances along the weld line.

Abstract: A friction stir additive welding device configured to join a first work-piece and second work-piece is provided. In one aspect, the device includes a nosecone including a channel extending along a central axis and configured to receive a rotating screw. The device also includes a slot including a first opening in an outer sidewall of the nosecone, a second opening in a wall of the channel of the nosecone, and a passageway through the nosecone between the first opening and the second opening. The slot is configured to receive a portion of a structure positioned between the first work-piece and the second work-piece.

Abstract: A method for optimizing machining parameters for a cutting process performed on a work piece. Finite element analysis of cutting tool and work material interaction is initially performed. Mechanistic modeling of the cutting process, using results of the finite element analysis, is then performed to provide optimized machining parameters for improved rate of material removal and tool life. Optionally, a two-stage artificial neural network may be supplementally employed, wherein a first stage of the network provides output parameters including peak tool temperature and cutting forces in X and Y directions, for a combination of input reference parameters including tool rake angle, material cutting speed, and feed rate.