johnwslee/injection_molding_analysis
Defect Classification in Injection Molding Using Machine Learning
This project helps manufacturing engineers and quality control managers identify defective injection-molded parts during production. It takes historical data of injection molding process parameters and part quality (pass/fail) to predict if a newly produced part is likely to be defective. This allows for early detection of issues and potentially prevents defective parts from reaching customers.
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Use this if you need to classify defective parts from injection molding processes using machine learning and want to explore different model approaches and their effectiveness.
Not ideal if you're looking for a plug-and-play solution for real-time, high-volume defect detection without any customization or understanding of machine learning principles.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 13, 2023
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