公开(公告)号：US20240416424A1

公开(公告)日：2024-12-19

申请号：US18334179

申请日：2023-06-13

Applicant: Raytheon Technologies Corporation

Inventor： Sergei F. Burlatsky ,  Andreas Karl Roelofs ,  Masoud Anahid ,  Ranadip Acharya ,  Tahany El-Wardany ,  David U. Furrer

IPC: B22F10/85 ,  B22F10/28 ,  B22F12/20 ,  B33Y10/00 ,  B33Y40/20 ,  B33Y50/02

Abstract: The present disclosure provides improved additive manufacturing methods and systems. More particularly, the present disclosure provides advantageous additive manufacturing methods and systems for the production of hierarchical design optimized components (e.g., composite or composite-like materials). The present disclosure provides a methodology to produce hierarchical design optimized additively manufactured parts/materials that include an inhomogeneous structure with variable local mechanical properties across the entire volume. Hierarchical inhomogeneous structure/composite materials can be produced through a laser powder bed fusion (LPBF) process. A novel LPBF method can be used to obtain location-specific properties through in-situ controlling of the local cooling rate during the additive manufacturing process.

公开(公告)号：US20240390983A1

公开(公告)日：2024-11-28

申请号：US18201787

申请日：2023-05-25

Applicant: Raytheon Technologies Corporation

Inventor： Masoud Anahid ,  Matthew E. Lynch ,  Malcolm P. MacDonald ,  Ranadip Acharya ,  Brian A. Fisher

IPC: B22F10/85 ,  B22F10/28 ,  B22F10/37

Abstract: A process for uncertainty quantification for a predictive defect model for multi-laser additive manufacturing of a part including executing computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; assigning a spatter particle size, velocity and direction relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; executing computational fluid dynamics post processing for spatter particle tracking; predicting a spatter particle landing pattern; feeding the spatter particle landing pattern prediction into a defect model; producing a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and predicting defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.