Can Artificial Intelligence Replace Classical Testing of Corrugated Board?

Abstract

For many years, laboratory testing has been the cornerstone of quality evaluation in the paper and corrugated board industry, supporting both material control and packaging design. However, growing production complexity and the need for faster technological decisions expose the limitations of traditional, time-intensive testing methods. This editorial explores the potential of artificial intelligence as a tool that can complement, rather than replace, classical approaches. Machine learning models are capable of estimating key strength parameters such as ECT and BCT, identifying anomalies, and supporting material selection based on process and environmental data. Nevertheless, their effectiveness depends strongly on data quality, and they cannot substitute for standardized tests required for validation and certification. It is argued that the most effective path forward is a hybrid model, combining the reliability of laboratory testing with the speed and predictive power of AI, leading to more efficient and informed decision-making.