# Collaborative Research: Improving Zero-Shot Learning of Manufacturing Anomalies by Leveraging Textual Sources

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Florida State University (FL) · $315,000

## 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 gap by developing an intelligent system that learns from existing engineering knowledge embedded in texts and images in professional documents to detect new and unforeseen anomalies. The proposed process does not rely solely on expensive or exhaustive measurements needed for traditional fault diagnostic methods. Thus, this project looks to strengthen domestic production capabilities and reduce dependence on manual inspection and expert-only knowledge. Furthermore, the project will engage STEM students in cutting-edge research at the intersection of artificial intelligence, natural language processing and manufacturing engineering. Through engagement with industry partners, students will gain hands-on experience with real-world challenges, preparing them for the advanced manufacturing workforce. Results will be shared broadly with the manufacturing community. In addition, industry seminars with 3D printer suppliers and automakers will support long-term technology transfer. 

The goal o

## Key facts

- **NSF award ID:** 2517645
- **Awardee organization:** Florida State University (FL)
- **SAM.gov UEI:** JF2BLNN4PJC3
- **PI:** Hui Wang
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Cybermanufacturing Systems, GRADUATE EDUCATION, Manufacturing, UNDERGRADUATE EDUCATION
- **Estimated total:** $315,000
- **Funds obligated:** $315,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2517645

## Citation

> US National Science Foundation, Award 2517645, Collaborative Research: Improving Zero-Shot Learning of Manufacturing Anomalies by Leveraging Textual Sources. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2517645. Licensed CC0.

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