Collaborative Research: Improving Zero-Shot Learning of Manufacturing Anomalies by Leverag — NSF Award to Florida State University
In advanced manufacturing systems, anomalies such as unexpected deviations from normal process behavior can lead to defective products or production disruptions. Detecting these anomalies early is essential for maintaining product quality and reducing waste. However, identifying such faults is challenging, especially w
| Award title | Collaborative Research: Improving Zero-Shot Learning of Manufacturing Anomalies by Leverag |
|---|---|
| Award ID | 2517645 |
| Awardee | Florida State University |
| City | TALLAHASSEE |
| State | FL |
| Amount obligated | $315,000 |
| Principal investigator | Hui Wang |
| Program | MSI-Manufacturing Systms Integ, Special Initiatives |
| Start date | 09/01/2025 |
| Abstract | In advanced manufacturing systems, anomalies such as unexpected deviations from normal process behavior can lead to defective products or production disruptions. Detecting these anomalies early is essential for maintaining product quality and reducing waste. However, identifying such faults is challenging, especially when their occurrence is rare and thus there is a lack of labeled data for conventional machine learning methods to recognize. This award supports research looking to address this g |
| Source | NSF Awards |
$799/mo
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