Real-time adaptive control of additive manufacturing processes using machine learning
US10539952B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 27, 2018 |
| Grant date | Jan 21, 2020 |
| Priority date | — |
| Expiry date | Dec 27, 2038 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02P10/25
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.