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The document discusses the application of Industry 4.0 data management and supervised machine learning to improve quality in low-pressure die casting (LPDC) processes, specifically for aluminum alloy automobile wheel castings. It highlights the challenges of predicting porosity defects due to the complexity of process variables and the need for optimized data collection and analysis methods. The study proposes using XGBoost classification models to enhance defect prediction accuracy and facilitate process p

Author

dinbandhu23

Language

EN