On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging

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 make 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 us 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.

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“…optimizing the homogenization process, which would simplify complex structures of corrugated board into manageable models (Garbowski 2022(Garbowski , 2023a, to refining the accuracy of safety factor estimations (Garbowski 2023a), crucial for ensuring structural integrity. Moreover, AI algorithms, particularly Artificial Neural Networks, as demonstrated in recent studies (Gajewski et al 2023;Gu et al 2023), have already shown significant potential in predicting the crush resistance and load-bearing capacity of various corrugated packaging designs.…”

Application of Artificial Neural Networks to Numerical Homogenization of the Precast Hollow-Core Concrete Slabs

“…Another example could be the paper of Staszak et al [13], in which it was shown that the precast concrete slabs reinforced with spatial linear trusses may be efficiently homogenized to one effective layer of a representative shell element. On the other hand, the computational time (ii) can be reduced by using soft computing methods such as the artificial neural network (ANN) algorithms [14][15][16][17][18] or metamodeling [19,20]. This usually requires a significant computational effort, which is incurred before the solution of the target computational problem is needed, but it allows one to obtain a solution with an acceptably small error in a fraction of a second when the actual problem occurs.…”

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“…optimizing the homogenization process, which would simplify complex structures of corrugated board into manageable models (Garbowski 2022(Garbowski , 2023a, to refining the accuracy of safety factor estimations (Garbowski 2023a), crucial for ensuring structural integrity. Moreover, AI algorithms, particularly Artificial Neural Networks, as demonstrated in recent studies (Gajewski et al 2023;Gu et al 2023), have already shown significant potential in predicting the crush resistance and load-bearing capacity of various corrugated packaging designs.…”