A Machine Learning-Based Production Defect Prediction and Process Optimization Framework using Random Forest

B Lavany, Dr.S.Usharani

Predictive Quality Management; Production Defect Prediction; Random Forest Classifier; Process Optimization; Industry 4.0; Machine Learning.

In modern manufacturing systems, ensuring consistent product quality across high-speed production lines is a major challenge due to equipment wear, process fluctuations, operator variability, and batch inconsistencies. Traditional quality control methods rely on manual inspection and post-production sampling, making them reactive and inefficient since defects are detected only after production. Predictive QualityX is a machine learning-based defect prediction and process optimization platform designed to enable proactive quality management. The system uses a Random Forest classifier trained on 25,000 synthetic production records generated from realistic process parameters, including temperature, pressure, vibration, rotational speed, machine ID, and operator ID across multiple machines and operators. The platform implements an end-to-end data pipeline that includes synthetic data generation, model training, evaluation, database integration using MySQL, and an interactive analytics dashboard. The trained model identifies complex nonlinear relationships between process variables and defect occurrence, enabling accurate prediction of product quality outcomes. The system also processes both historical and future production data to provide retrospective analysis and forward-looking defect risk estimation. Results are stored in Excel and MySQL databases for reporting and enterprise integration. An interactive dashboard visualizes key insights such as machine-wise defect rates, parameter correlations, and temporal defect trends. This enables engineers and managers to identify high-risk conditions, optimize processes, and reduce production losses, making Predictive QualityX a comprehensive intelligent manufacturing decision-support system.

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