US 12,459,037 B2
Structural simulation of additively manufactured components
Matthew James Prentice, Preston (GB); Steven Robert Barnes, Balderstone (GB); Austin James Cook, Balderstone (GB); and Stephen Arthur Morgan, Bristol (GB)
Assigned to BAE Systems plc, London (GB)
Appl. No. 18/011,769
Filed by BAE Systems plc, London (GB)
PCT Filed Jun. 4, 2021, PCT No. PCT/GB2021/051399
§ 371(c)(1), (2) Date Dec. 20, 2022,
PCT Pub. No. WO2021/260340, PCT Pub. Date Dec. 30, 2021.
Claims priority of application No. 20275113 (EP), filed on Jun. 25, 2020; and application No. 2009704 (GB), filed on Jun. 25, 2020.
Prior Publication US 2023/0264264 A1, Aug. 24, 2023
Int. Cl. B22F 10/28 (2021.01); B22F 10/25 (2021.01); B29C 64/393 (2017.01); B22F 10/80 (2021.01); B22F 10/85 (2021.01); B33Y 10/00 (2015.01); B33Y 30/00 (2015.01); B33Y 40/00 (2020.01); B33Y 50/00 (2015.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G16C 60/00 (2019.01)
CPC B22F 10/28 (2021.01) [B22F 10/25 (2021.01); B29C 64/393 (2017.08); B22F 10/85 (2021.01); B33Y 10/00 (2014.12); B33Y 30/00 (2014.12); B33Y 40/00 (2014.12); B33Y 50/00 (2014.12); G06N 7/01 (2023.01); G16C 60/00 (2019.02)] 11 Claims
OG exemplary drawing
 
1. A method of estimating a mechanical property of an article manufactured, at least in part, by additive manufacturing, AM, the method implemented, at least in part, by a computer comprising a processor and a memory, the method comprising:
obtaining a set of in-process parameters of the AM of the article, wherein respective parameters of the set thereof are spatially resolved, having positional information associated therewith and wherein obtaining the set of in-process parameters of the AM of the article comprises monitoring optical, thermal, and/or acoustic emissions during the AM of the article;
inferring a set of attributes of the article corresponding to the set of in-process parameters, wherein respective attributes of the set thereof are spatially resolved, having the positional information associated therewith and wherein the respective attributes of the set thereof are spatially resolved mechanical properties of the article; and
estimating the mechanical property of the article based, at least in part, on the inferred set of attributes;
wherein the mechanical property of the article is a fatigue life of the article;
wherein the spatially resolved mechanical properties of the article are selected from a group consisting of: a compressive strength, a creep, an elasticity, an elastic limit, a fatigue life, a fatigue limit, a flexibility, a flexural modulus, a flexural strength, a fracture toughness, a hardness, a shear modulus, a shear strength, a specific modulus, a specific strength, a stiffness, a tensile strength, a toughness, an ultimate tensile strength, a yield strength and a Young's modulus;
wherein inferring the set of attributes of the article corresponding to the set of in-process parameters comprises inferring the set of attributes of the article corresponding to the set of in-process parameters using a trained machine learning, ML, algorithm; and
wherein estimating the property of the article based, at least in part, on the inferred set of attributes comprises finite element analysis, FEA, of the article using the inferred set of attributes.