Author(s): Amit Sharma

Abstract: The print manufacturing industry is on the brink of digital transformation, driven by the rise of Industry 4.0 and the growing need for intelligent, data-driven operations. This paper proposes a domain-specific framework for integrating digital twin technology with predictive analytics to create smart print factories. Focusing on offset and digital printing systems, the study presents a modular architecture that captures real-time telemetry from print assets—such as ink systems, feeders, and registration units—and processes it using machine learning models to predict equipment failures and detect quality deviations. A prototype simulation of a Heidelberg Speedmaster XL 106 press validates the feasibility of this framework, demonstrating up to 30% reduction in unplanned downtime, improved print consistency, and substantial material savings. The literature review identifies a clear gap in print-specific digital twin applications, particularly for predictive maintenance and quality control. Addressing this void, the proposed framework offers actionable pathways for adoption, supported by cloud-edge computing, ERP/MIS integration, and scalable AI models. The paper concludes with recommendations for pilot deployments and future research to standardize digital twin maturity models tailored to the printing sector.

DOI: 10.33545/27076571.2025.v6.i2a.172

Pages: 16-22 | Views: 287 | Downloads: 132

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