A Comparison of Two Artificial Intelligence Approaches for Corrugated Board Type Classification

Abstract

Corrugated board is an environmentally friendly, commonly used packing material. Its basic structure consists of two liners and a flute between them. The mechanical properties and strength of the corrugated board depend not only on the constituent papers but also its geometry, which can be distorted, however, due to various factors related to its manufacturing process or use. The greatest distortion occurs in the corrugated layer, which, due to crushing, significantly deteriorates the functional properties of cardboard. In this work, two algorithms for the automatic classification of corrugated board types based on images of deformed corrugated boards using artificial intelligence methods are presented. A prototype of a corrugated board sample image acquisition device was designed and manufactured. It allowed for the collection of an extensive database of images with corrugated board cross-sections of various types. Based on this database, two approaches for processing and classifying them were developed. The first method is based on the identification of the geometric parameters of the corrugated board cross-section using a genetic algorithm. After this stage, a simple feedforward neural network was applied to classify the corrugated board type correctly. In the second approach, the use of a convolutional neural network for corrugated board cross-section classification was proposed. The results obtained using both methods were compared, and the influence of various imperfections in the corrugated board cross-section was examined.

Keywords

• corrugated board
• cross-section image
• genetic algorithm
• feedforward neural network
• convolutional neural network