The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 23, 2024

Filed:

May. 07, 2019
Applicant:

Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US);

Inventors:

Tsuyoshi Nomura, Novi, MI (US);

Atsushi Kawamoto, Nagakute, JP;

Yoshihiro Iwano, Toyota, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 30/20 (2020.01); G06F 30/23 (2020.01); G06F 111/04 (2020.01); G06F 111/10 (2020.01); G06F 111/20 (2020.01);
U.S. Cl.
CPC ...
G06F 30/20 (2020.01); G06F 30/23 (2020.01); G06F 2111/04 (2020.01); G06F 2111/10 (2020.01); G06F 2111/20 (2020.01);
Abstract

A method for optimizing orientations of an anisotropic material in a component. For example, the method overcomes the non-uniqueness and gimbal locking problems associated with using Euler angles to define the orientation by instead parameterizing the orientation using an orientation tensor that is a self-dyadic product of a direction vector. To avoid non-linear constraints in the mathematical design variables used in the optimization, isoparametric shape functions map the mathematical design variables to physical design variables, and the mapping ensures that various constraints associated with tensor invariants of the orientation tensor are satisfied even though these constraints are not directly imposed on the mathematical design variables. The physical design variables are used to model the component, whereas optimization is performed using the mathematical design variables. Thus, optimization is greatly simplified by removing the tensor-invariant constraints from the optimization step to the mapping step.


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