Corrugated box damage prediction using artificial neural network image training

This thesis proposes a novel packaging evaluation method using corrugated box images and an Artificial Neural Network (ANN). An Artificial Neural Network works in a way similar to that of neurons in a human brain: by making connections between a trained dataset and the new data provided after training. The ANN has been implemented in the industry in various ways but limited in the packaging evaluation. This paper is focused on the corrugated box damage prediction using ANN. By capturing the damaged corrugated box images with an Artificial Neural Network, 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 Artificial Neural Network 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 Artificial Neural Network, 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.