Summary of Enhancing Manufacturing Quality Prediction Models Through the Integration Of Explainability Methods, by Dennis Gross et al.
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
by Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 The proposed method leverages explainability techniques to enhance the performance of machine learning (ML) models in predicting milling process quality, as showcased through a real-world manufacturing scenario. By training ML models and then fine-tuning them by removing irrelevant features identified using explainability methods, the approach achieves performance improvements. This study demonstrates the value of explainability techniques in not only interpreting but also optimizing predictive models in manufacturing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special computer tools to make machine learning models better at predicting how well a manufacturing process works. It does this by training the models and then making them focus on the most important things, rather than getting distracted by unnecessary details. This makes the models more accurate and can even help reduce costs in real-world factories. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning
