Generative design techniques for automobile designs
In various embodiments, a generative design application generates and evaluates automotive designs. In operation, the generative design application computes a first set of metric values based on a set of metrics associated with design goal(s) and a first set of parameter values for a parameterized automobile model. The generative design application then performs optimization operation(s) on the first set of parameter values based on the first set of metric values to generate a second set of parameter values. Subsequently, the generative design application generates at least one design based on the second set of parameter values that is more convergent with respect to at least one of the design goals than a previously generated design. Advantageously, less time and effort are required to generate and evaluate multiple designs and then optimize those designs relative to more manual prior art approaches.
1. A computer-implemented method for generating automotive designs, the method comprising:
computing a first plurality of metric values based on a first plurality of parameter values for a parameterized automobile model that is associated with at least one previously-generated design;
performing one or more optimization operations on the first plurality of parameter values to generate a second plurality of parameter values;
generating, based on the second plurality of parameter values, a plurality of production designs, wherein at least one design included in the plurality of production designs is more convergent with respect to at least one design goal than the at least one previously-generated design; and
displaying a design space that includes at least one or more of the production designs included in the plurality of production designs.
2. The computer-implemented method of claim 1 , where the first plurality of metric values is further computed based on a plurality of metrics associated with one or more design goals.
3. The computer-implemented method of claim 2 , wherein a first design goal included in the one or more design goals is associated with at least one of performance, body design, chassis design, interior design, exterior styling, aerodynamics, crash criteria, or manufacturability.
4. The computer-implemented method of claim 2 , wherein the one or more design goals are included in a goal dataset that defines one or more aspects of an automotive design.
5. The computer-implemented method of claim 1 , wherein the parameterized automobile model is further associated with a goal dataset that defines one or more aspects of an automotive design.
6. The computer-implemented method of claim 1 , wherein computing the first plurality of metric values comprises:
generating a first computer-aided design (CAD) geometry model based on the first plurality of parameter values and the parameterized automobile model; and
for each metric included in the first plurality of metrics, computing, a metric value for the metric based on the first CAD geometry model.
7. The computer-implemented method of claim 1 , wherein performing the one or more optimization operations comprises executing a metaheuristic search algorithm based on:
the first plurality of parameter values,
the first plurality of metric values, and
one or more optimization types associated with one or more design goals.
8. The computer-implemented method of claim 7 , wherein a first optimization type included in the one or more optimization types specifies at least one of a minimization, a maximization, a target range, or a Boolean associated with a pass/fail condition.
9. The computer-implemented method of claim 1 , wherein a first metric included in the first plurality of metrics comprises a structural metric, a performance-related metric, or a program management metric.
10. The computer-implemented method of claim 1 , wherein a first parameter value included in the second plurality of parameter values is associated with a first instance of a first parameterized automobile component, and a second parameter value included in the second plurality of parameter values is associated with a sub-instance of the first instance.
11. One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:
computing a first plurality of parameter values for a parameterized automobile model that is associated with at least one previously-generated design;
performing one or more optimization operations on the first plurality of parameter values to generate a second plurality of parameter values;
generating, based on the second plurality of parameter values, a plurality of production designs, wherein at least one design included in the plurality of production designs is more convergent with respect to at least one design goal than the at least one previously-generated design; and
displaying a design space that includes the at least one design.
12. The one or more non-transitory computer-readable media of claim 11 , further comprising computing a first plurality of metric values based on the first plurality of parameter values and a plurality of metrics associated with one or more design goals.
13. The one or more non-transitory computer-readable media of claim 12 , wherein a first design goal included in the one or more design goals is associated with at least one of performance, body design, chassis design, interior design, exterior styling, aerodynamics, crash criteria, or manufacturability.
14. The one or more non-transitory computer-readable media of claim 12 , wherein the one or more design goals are included in a goal dataset that defines one or more aspects of an automotive design.
15. The one or more non-transitory computer-readable media of claim 11 , wherein the parameterized automobile model is further associated with a goal dataset that defines one or more aspects of an automotive design.
16. The one or more non-transitory computer-readable media of claim 11 , further comprising computing a first plurality of metric values based on the first plurality of parameter values, wherein computing the first plurality of metric values comprises:
generating a first computer-aided design (CAD) geometry model based on the first plurality of parameter values and the parameterized automobile model; and
for each metric included in the first plurality of metrics, computing, a metric value for the metric based on the first CAD geometry model.
17. The one or more non-transitory computer-readable media of claim 11 , further comprising computing a first plurality of metric values based on the first plurality of parameter values, wherein performing the one or more optimization operations comprises executing a metaheuristic search algorithm based on:
the first plurality of parameter values,
the first plurality of metric values, and
one or more optimization types associated with one or more design goals.
18. The one or more non-transitory computer-readable media of claim 17 , wherein a first optimization type included in the one or more optimization types specifies at least one of a minimization, a maximization, a target range, or a Boolean associated with a pass/fail condition.
19. The one or more non-transitory computer-readable media of claim 11 , further comprising computing a first plurality of metric values based on the first plurality of parameter values, wherein a first metric included in the first plurality of metrics comprises a structural metric, a performance-related metric, or a program management metric.
20. The one or more non-transitory computer-readable media of claim 11 , wherein a first parameter value included in the second plurality of parameter values is associated with a first instance of a first parameterized automobile component, and a second parameter value included in the second plurality of parameter values is associated with a sub-instance of the first instance.
21. A system, comprising:
one or more memories storing instructions; and
one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
compute a first plurality of parameter values for a parameterized automobile model,
perform one or more optimization operations on the first plurality of parameter values to generate a second plurality of parameter values,
generate, based on the second plurality of parameter values, a plurality of production designs, and
display a design space that includes one or more of the production designs included in the plurality of production designs.