Corrugated Box Damage Classification Using Artificial Neural Network Image Training

Abstract

This paper proposes a novel packaging evaluation method using corrugated box images and an artificial neural network (ANN). An ANN works in a way similar to that of neurons in a human brain: by making connections between a trained dataset and the new images provided after training. The ANN has been implemented in the industry in various ways but limited in packaging evaluation. This paper is focused on the corrugated box damage prediction using ANN with the Orange Data Mining platform. By capturing the damaged corrugated box images with an ANN, damaged products can be identified allowing a decision to be made as to what type of package failure occurred. One of the benefits to using an ANN to evaluate corrugated box images is that it allows for the evaluation of package protection in a real distribution environment as compared to a controlled lab setting. In turn, this reduces the cost of testing, as the package failure will have been identified with the assistance of the ANN, rather than full retesting to identify where damage occurred. This process would also reduce costs associated with the usage of materials for testing, due to the lower number of test samples required.