Incorporating change diagnosis using probabilistic tensor regression model for improving processing of materials
US10754310B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Oct 18, 2018 |
| Grant date | Aug 25, 2020 |
| Priority date | — |
| Expiry date | Nov 27, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/45031
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A probability distribution of a manufacturing system's performance conditioned on a training dataset comprising a historical tensor and associated performance metric of a reference period is learned. An input tensor associated with a time window and the input tensor's associated performance metric may be received. The input tensor includes at least multiple sensor variables associated with the manufacturing system and multiple steps of the manufacturing system's manufacturing process. Based on the probability distribution, an overall change is determined between the training dataset's relationship of the historical tensor and associated performance metric, and the relationship of the input tensor and the input tensor's associated performance metric. Based on the probability distribution, contribution of at least one of the multiple variables and the multiple steps to the overall change is determined. An action is automatically triggered in the manufacturing system which reduces the overall change.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.