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About Machine Learning Case Studies For Quality Evaluations
The document discusses TimkenSteel's use of machine learning and MATLAB for image-based steel inspection and process optimization. Specifically, it details projects involving automated classification of non-metallic inclusions in images and development of a pin stamp reader to automate identification checks. The automated inclusion classification system achieves human-level accuracy of around 93% using a custom convolutional neural network model trained on hundreds of inclusion images. The system provides a fully automated analysis that is faster and more consistent than human experts. Work is ongoing to achieve over 95% accuracy, especially for difficult inclusion types. An automated pin stamp reader is also in development to eliminate potential for human error in product tracking.
Detailed Information
| Author: | ['Michael Mejia'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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