Summary of Machine Learning-based Layer-wise Detection Of Overheating Anomaly in Lpbf Using Photodiode Data, by Nazmul Hasan et al.
Machine Learning-based Layer-wise Detection of Overheating Anomaly in LPBF using Photodiode Data
by Nazmul Hasan, Apurba Kumar Saha, Andrew Wessman, Mohammed Shafae
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework is proposed to detect overheating anomalies in laser powder bed fusion additive manufacturing using photodiode sensor data. The method extracts three sets of features from the raw data and trains several machine learning classifiers, including cost-sensitive learning to handle class imbalance. A majority voting ensemble approach is used to boost detection accuracy. The case study demonstrates that the proposed method achieves superior results in detecting overheating anomalies, surpassing existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Overheating anomaly detection is important for quality and reliability in laser powder bed fusion additive manufacturing. Researchers have developed a new way to use photodiode sensor data to detect overheating anomalies layer by layer. They extract different features from the data and train machine learning models to classify layers as normal or affected by overheating. The new method is better than existing methods at detecting overheating. |
Keywords
* Artificial intelligence * Anomaly detection * Machine learning
