Predictive Maintenance for Digital Printing Equipment

This study applies a predictive maintenance model of digital printing equipment based on the concept of IoT-enabled sensing, machine learning analytics, and digital twin simulation that allows predicting faults in real time and optimizing maintenance. A CNNLSTM hybrid model was designed and used to forecast faults and Remaining Useful Life (RUL) by analyzing vibration, temperature, acoustic, and optical data. Multi-sensor printing testbed experimental implementation showed high predictive accuracy (R 2 = 0.94, F1 = 0.93), which decreased the unplanned downtime by 32, maintenance cost by 24, and material waste by 18. The virtual copy of the printing system of digital twin was a dynamic one that enabled continuous synchronization, what-if analysis, and the creation of adaptive alerts. The suggested architecture is environmentally friendly, as it optimizes the energy consumption, increases the lifespan of the components, and reduces the waste which will also meet the Industry 4.0 and smart manufacturing goals. This study defines predictive maintenance as a scalable, cost effective, and environmentally friendly approach of the next generation digital printing ecosystem

Digital manufacturing · Digital printing · Downtime · Industry 4.0 · Model predictive control · Overall equipment effectiveness · Predictive maintenance · Testbed · Digital Transformation in Industry · Material Properties and Processing · Sustainable Supply Chain Management