AI-AUGMENTED DMAIC FRAMEWORK FOR MANUFACTURING QUALITY IMPROVEMENT - A CASE STUDY USING PUBLIC DATASET

Abstract

Manufacturing industries are increasingly adopting intelligent technologies to improve product quality, minimize production defects, and enhance operational efficiency in highly competitive industrial environments. Traditional quality management methodologies such as Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) have been widely used for systematic process improvement and defect reduction. However, conventional DMAIC approaches mainly rely on statistical analysis and manual decision-making, which often become insufficient when dealing with large-scale industrial datasets, real-time sensor streams, and complex manufacturing systems associated with Industry 4.0. To address these challenges, this research proposes an AI-augmented DMAIC framework that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques into the traditional DMAIC methodology for intelligent manufacturing quality improvement. The proposed framework enhances each DMAIC phase by incorporating predictive analytics, automated defect detection, root cause analysis, and data-driven decision support. A public manufacturing quality dataset containing operational machine parameters and defect-related information is utilized as a case study to validate the effectiveness of the proposed approach. In the proposed system, data preprocessing and feature engineering techniques are first applied to prepare the manufacturing dataset for analysis. Subsequently, Machine Learning models including Random Forest and Neural Network classifiers are trained to predict defective products and identify the most influential manufacturing parameters affecting quality performance. Various evaluation metrics such as Accuracy, Precision, Recall, F1-Score, and Mean Squared Error (MSE) are used to assess model performance. Experimental results demonstrate that the AI-enhanced DMAIC framework significantly improves manufacturing quality by reducing defect rates, minimizing process variation, and increasing predictive accuracy. Among the implemented models, the Random Forest classifier achieved the highest performance with superior defect prediction capability and efficient feature importance analysis. The findings further indicate that integrating AI within DMAIC enables proactive quality management, intelligent process optimization, and real-time monitoring in smart manufacturing environments. The proposed framework provides a scalable, adaptive, and data-driven quality improvement solution suitable for Industry 4.0 applications. This research contributes toward the development of intelligent manufacturing systems capable of autonomous decision-making and continuous operational improvement