Gajewski, T, Grabski, JK, Cornaggia, A, Garbowski, T. On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging. Packag Technol Sci. 2023; 1-9. doi:10.1002/pts.2783
Gajewski, T, Grabski, JK, Cornaggia, A, Garbowski, T. On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging. Packag Technol Sci. 2023; 1-9. doi:10.1002/pts.2783
Gajewski, T, Grabski, JK, Cornaggia, A, Garbowski, T. On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging. Packag Technol Sci. 2023; 1-9. doi:10.1002/pts.2783
Gajewski, T, Grabski, JK, Cornaggia, A, Garbowski, T. On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging. Packag Technol Sci. 2023; 1-9. doi:10.1002/pts.2783
Abstract
Artificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations, and typical flap boxes, which makes the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging, but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
