Machine learning with fast feature generation for selective laser melting print parameter optimization
US11487271B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 22, 2020 |
| Grant date | Nov 1, 2022 |
| Priority date | — |
| Expiry date | Dec 13, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02P10/25
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method includes identifying machine process parameters for an additive manufacturing process to produce a part, providing a real-world sensor to sense a characteristic associated with a real-world version of the additive manufacturing process, receiving sensor readings from the real-world sensor while the machine is performing the real-world version of the additive manufacturing process, generating, with a computer-based processor, physics-based features associated with the additive manufacturing process, and training a machine-learning software model based at least in part on the machine process parameters, the sensor readings, and the physics-based features to predict a behavior of the real-world sensor.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.