DOI: 10.21541/apjess.1945333 ISSN: 2147-4575

An Interpretable Machine Learning Framework for Predicting Quality Outcomes in Printing Production Systems

Selin Sayrım, Kerem Ocak, Serap Ercan Cömert
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This study proposes an interpretable machine learning framework for predicting multiple quality outcomes in a printing production process characterized by complex and nonlinear relationships. The analysis is based on a real-world industrial dataset including key process parameters, namely Ink Weight, Top Coat Weight, Printing Speed, Varnish GC, and Temperature, along with two quality indicators: Solvent Residue and Isooctane Ratio. To capture nonlinear patterns and interaction effects, several machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were implemented and comparatively evaluated. The proposed framework integrates data preprocessing, polynomial feature expansion, and cross-validation-based hyperparameter optimization to ensure robustness and generalization capability. The results demonstrate that nonlinear and instance-based learning approaches outperform conventional models in representing the underlying data structure. In particular, KNN achieves the highest predictive performance across both target variables, with R² values of 0.9623 for Solvent Residue and 0.9094 for Isooctane Ratio, indicating strong agreement between predicted and observed values. To enhance interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted to quantify the contribution of each input variable to model predictions. The findings reveal that process-quality relationships are highly nonlinear and governed by complex interaction effects across different operating conditions. The proposed framework contributes to the literature by enabling the simultaneous prediction of multiple quality indicators, providing a robust and interpretable decision-support tool for data-driven process optimization in printing production systems.

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