Title: Using machine learning methods for pre-printing ink consumption estimation

Authors: Yavuz Selim Balcioğlu; Muhammed Mehmet Alper; Bülent Sezen

Addresses: Department of MIS, Dogus University, Umraniye, Istanbul, 34775, Turkey ' Department of Business Administration, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey ' Department of Business Administration, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey

Abstract: Accurately estimating ink consumption in the packaging printing industry is crucial for minimising waste, improving efficiency, and promoting sustainability. Traditional estimation methods rely on operator intuition, often leading to overproduction and increased costs. This study applies machine learning to predict pre-printing ink consumption using a dataset of 77 samples from a domestic packaging printing company. Key numerical and categorical features, including paper size, halftone dot percentage, ink type, and paper properties, were analysed. Multiple machine learning models were tested, with XGBoost outperforming others, achieving a mean absolute error (MAE) of 0.0026 g, root mean squared error (RMSE) of 0.0035 g, and an R-squared value of 0.619. Implementing XGBoost led to an average ink savings of 2,380 g per job. These findings highlight the potential of machine learning, particularly XGBoost, in optimising ink consumption. Future research will explore additional paper properties and customised objective functions to enhance prediction accuracy.

Keywords: ink consumption estimation; machine learning; packaging printing; XGBoost; CART-based models.

DOI: 10.1504/IJEDPO.2025.152136

International Journal of Experimental Design and Process Optimisation, 2025 Vol.8 No.1/2, pp.62 - 107

Received: 01 Jun 2025
Accepted: 18 Nov 2025
Published online: 09 Mar 2026 *

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