Modern print & packaging technologies are equipped with tightly meshed sensor networks, inline inspection systems and digital measurement, control and regulation technology. Deep Learning and Artificial Intelligence now offer a set of tools to reap the harvest. They extract information from the wealth of production and machine data, which grows by the second, that indicates impending quality problems, looming malfunctions, machine wear, or hidden causes of recurring defects. Such condition monitoring, based on continuous data evaluation, makes it possible to fully exploit the service life of wear parts and operating materials, to proactively plan and synchronize repairs and maintenance, and thus to minimize machine and plant downtime. But the potential of intelligent data evaluation methods is far from exhausted. Artificial intelligence is far better at recognizing complex relationships in large amounts of data than the human brain.
The Print & Packaging Community uses this advantage to compare anonymized data from many millions of production cycles of thousands of users in order to leverage untapped productivity potential through this AI-supported benchmarking. Thanks to deep learning methods, inline inspection systems reliably do their job even when printing systems are running at top speed. In paper production, AI combines findings from continuous process data analyses with documented empirical knowledge from process engineers in order to be able to operate systems trouble-free even in borderline areas. Where precise web guiding is required in winding, slitting and printing processes, learning systems help to overcome the limits of mechanics. For this purpose, they continuously analyze the smallest control deviations and calculate how these can be compensated for in the next cycle - AI-supported precontrol thus ensures that tolerable deviations do not become errors. In printing processes, smart algorithms ensure that the required color saturation is achieved with minimal ink input. This also serves to increase cost and resource efficiency, as does the use of machine learning in packaging design. Based on comparative simulations, the systems run through various designs at lightning speed and determine the one that guarantees the highest output of visually appealing, stable and fully recyclable packaging with the lowest material input.
These are the first steps in a megatrend that will change the print & packaging industry on many levels in the coming decades. Wherever complexity is too high for human thinking, data volumes too large and processes too fast, AI will sooner or later prevail. But this takes time because the systems do not possess intuitive intelligence. Rather, they have to be trained for their respective tasks at great expense before they can independently deepen and refine this learned knowledge in day-to-day practice. But the effort is worth it, because once AI has been learned, it improves process quality without tiring, without being day-to-day, and with steadily increasing performance. The areas of application range from optimized design and layout in prepress, the combination of mass customization and automation, or optimized production control for a large number of different print jobs, to closed control loops throughout the entire process chain or data-based predictive maintenance that minimizes unexpected production downtime.