US 12,169,398 B2
Generative design shape optimization based on a target part reliability for computer aided design and manufacturing
Andrew John Harris, London (GB); Adrian Adam Thomas Butscher, Toronto (CA); Allin Irving Groom, London (GB); and Konara Mudiyanselage Kosala Bandara, Beckenham (GB)
Assigned to Autodesk, Inc., San Francisco, CA (US)
Filed by Autodesk, Inc., San Francisco, CA (US)
Filed on Aug. 27, 2021, as Appl. No. 17/459,710.
Prior Publication US 2023/0088537 A1, Mar. 23, 2023
Int. Cl. G05B 19/4099 (2006.01); G05B 19/4093 (2006.01); G06F 30/17 (2020.01)
CPC G05B 19/4099 (2013.01) [G05B 19/40932 (2013.01); G05B 19/40938 (2013.01); G06F 30/17 (2020.01)] 39 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining a design space for a modeled object, for which a corresponding physical structure is to be manufactured using one or more materials, and design criteria for the modeled object including one or more loading cases for numerical simulation of the physical structure and at least one design constraint on an acceptable likelihood of failure for the physical structure, wherein a statistical model that relates a structural performance metric to specific likelihoods of failure for the one or more materials is used to translate between the acceptable likelihood of failure and a value for the structural performance metric;
iteratively modifying a generatively designed three dimensional shape of the modeled object in the design space in accordance with the design criteria including the one or more loading cases for the numerical simulation of the physical structure and the at least one design constraint to stay under the acceptable likelihood of failure for the physical structure, wherein the numerical simulation includes computing the structural performance metric, which is evaluated against the at least one design constraint; and
providing the generatively designed three dimensional shape of the modeled object for use in manufacturing the physical structure;
wherein the at least one design constraint specifies a maximum likelihood of failure, obtaining the at least one design constraint comprises setting the maximum likelihood of failure based on the acceptable likelihood of failure for the physical structure, and the iteratively modifying comprises evaluating the maximum likelihood of failure at each of multiple different locations on or in the modeled object by calculating a likelihood of failure at the location using the statistical model and a value for the structural performance metric indicated for the location by the numerical simulation in accordance with one or more specific geometric parameters of the modeled object at the location.