公开(公告)号：US20170361377A1

公开(公告)日：2017-12-21

申请号：US15532273

申请日：2015-12-02

Applicant: Moog Inc.

Inventor： Paul Guerrier ,  Ian L. Brooks

IPC: B22F3/105 ,  B33Y40/00 ,  B33Y10/00 ,  B33Y30/00 ,  C01G23/02

CPC classification number: B22F3/1055 ,  B22F2003/1056 ,  B22F2003/1059 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y40/00 ,  C01G1/06 ,  C01G23/02 ,  Y02P10/295

Abstract: An additive manufacturing system with a build chamber has a halide vessel that generates a halide gas and a dissociation chamber with a filament. Metal condensate is contacted with the halide gas to form a gaseous metal halide compound. The gaseous metal halide compound is decomposed to deposit metal on the filament. In an example, titanium reacts with gaseous iodine to form gaseous titanium tetraiodide.

公开(公告)号：US20170246709A1

公开(公告)日：2017-08-31

申请号：US15509019

申请日：2015-09-17

Applicant: Moog Inc.

Inventor： Paul Guerrier ,  Ian L. Brooks

IPC: B23K26/16 ,  B23K26/12 ,  B33Y10/00 ,  B29C67/00 ,  B33Y40/00 ,  B23K26/70 ,  B22F3/105 ,  B23K26/342 ,  B33Y30/00

CPC classification number: B23K26/16 ,  B22F3/1055 ,  B22F2003/1056 ,  B22F2003/1059 ,  B22F2998/10 ,  B23K26/08 ,  B23K26/123 ,  B23K26/127 ,  B23K26/342 ,  B23K26/702 ,  B23K26/706 ,  B29C64/153 ,  B29C64/20 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y40/00 ,  Y02P10/295

Abstract: Byproduct condensate generated during additive manufacturing is controlled by providing at least one electrode inside a chamber. The condensate may be electrically charged as it is generated or an electrical charge may be imparted to the condensate. The electrode may have either a positive or negative bias to either attract or repel the condensate. The electrode may be located on a wall of the chamber or associated with a transparent window through which a laser beam passes into the chamber.

公开(公告)号：US11112771B2

公开(公告)日：2021-09-07

申请号：US16955334

申请日：2018-12-15

Applicant: MOOG INC.

Inventor： Paul Guerrier ,  George Baggs

IPC: G05B19/4099 ,  B33Y30/00 ,  B33Y50/02 ,  B29C64/393 ,  G06N20/00

Abstract: An additive manufacturing system uses a trained artificial intelligence module as part of a closed-loop control structure for adjusting the initial set of build parameters in-process to improve part quality. The closed-loop control structure includes a slow control loop taking into account in-process build layer images, and may include fast control loop taking into account melt pool monitoring data. The artificial intelligence module is trained using outputs from a plurality of convolutional neural networks (CNNs) tasked with evaluating build layer images captured in-process and images of finished parts captured post-process. The post process images may include two-dimensional images of sectioned finished parts and three-dimensional CAT scan images of finished parts.