Enhancing sustainable food packaging design: A machine learning approach to predict ventilated corrugated paperboard strength

Abstract

In the food packaging industry, ventilated corrugated paperboard boxes are crucial for sustainable transport of fresh products. While these boxes' ventilation holes advance air circulation, they also impact the material's compression or buckling strength. Variations in hole geometry and location affecting this strength are explored, considering the composite material, multi-layered structure. Traditional mechanical analyses, which often require simplifications, may not fully capture this complexity, leading to less accurate predictions of the paperboard's strength. To address these challenges, a machine learning (ML) approach was utilized, employing the Light Gradient Boosting Machine (LGBM) to develop a predictive model for the buckling strength of corrugated paperboard boxes with ventilation cutouts. This physics-informed ML model, trained on a compression dataset resulting from experimental tests for plates with a single cutout in three shapes and Finite Element Method (FEM) simulations for plates with various patterns of circular cutouts, provides highly accurate estimates of the plates' buckling strength. It achieved 91.7% accuracy on experimental data and 94.68% on FEM simulation data, showcasing its reliability. A new tool for predicting the buckling strength of corrugated paperboard is provided by this research, along with insights that can inform the design of more sustainable packaging solutions. Furthermore, the methodology and findings have broader applications, potentially benefiting sectors like aerospace and construction, where similar structural materials are used.