Sensitivity Analysis of Open-Top Cartons in Terms of Compressive Strength Capacity

Mrówczyński, Damian; Gajewski, Tomasz; Garbowski, Tomasz

doi:10.3390/ma16010412

Cited by 7 publications

(16 citation statements)

References 23 publications

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“…Over the years, starting in the 1950s, the analytical formulas for typical box design have been developed [ 21 , 22 , 23 ], subsequently expanding into more complex approaches [ 7 , 19 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Hybrid [ 7 , 10 , 24 , 25 , 32 , 33 ] or purely numerical approaches [ 3 , 34 , 35 ] with the implementation of the finite element method (FEM) [ 2 , 3 , 36 , 37 , 38 ] are widely applicable as well. Considering the layered structure of the cardboard and the anisotropy of the paper, performing numerical analysis is a challenging task since the knowledge of material properties of each layer is a precondition.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

2023

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Recently, AI has been used in industry for very precise quality control of various products or in the automation of production processes through the use of trained artificial neural networks (ANNs) which allow us to completely replace a human in often tedious work or in hard-to-reach locations. Although the search for analytical formulas is often desirable and leads to accurate descriptions of various phenomena, when the problem is very complex or when it is impossible to obtain a complete set of data, methods based on artificial intelligence perfectly complement the engineering and scientific workshop. In this article, different AI algorithms were used to build a relationship between the mechanical parameters of papers used for the production of corrugated board, its geometry and the resistance of a cardboard sample to edge crushing. There are many analytical, empirical or advanced numerical models in the literature that are used to estimate the compression resistance of cardboard across the flute. The approach presented here is not only much less demanding in terms of implementation from other models, but is as accurate and precise. In addition, the methodology and example presented in this article show the great potential of using machine learning algorithms in such practical applications.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

2023

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“…Due to the modified support conditions in the box compression test, in the middle of the sidewalls’ span, the effective stresses are very small. This effect can be well captured in comparison to the work of Mrówczyński et al [ 38 ], where the standard box compression test was carried out. Due to the full support of the bottom in the standard test, high effective stress values are also reached beyond the corner zones.…”

Section: Discussionmentioning

confidence: 57%

“…The sensitivity of the parameters in the modified box compression test can be compared to the results obtained from the standard approach [ 38 ]. The top edge lengths

, and

are the most significant in both methods, because the wider the load-bearing panels are, the more material is involved in the bearing zone of each corner.…”

Section: Discussionmentioning

confidence: 99%

“…The arbitrarily given choice of the size of the finite elements is based on the experience presented in previous works, where a thorough analysis of the influence of the mesh size on the results of the numerical analyses of various trays has already been carried out and presented [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…ANNs have a wide area of applications [ 32 , 33 , 34 , 35 ], including the food and packaging industry [ 36 , 37 ]. They can also be efficiently used as a tool for sensitivity analysis [ 38 ], as performed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Modified Compression Test of Corrugated Board Fruit Tray: Numerical Modeling and Global Sensitivity Analysis

2023

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This article presents a modified configuration of the box compression test (BCT), which reflects the actual behavior of the vegetable or fruit trays during transport and storage. In traditional load capacity tests, trays are treated as classic transport boxes, i.e., they are compressed between two rigid plates, which does not take into account the specific geometry of this type of packaging. Both the boundary conditions and the loads acting on the tray were modified. The paper presents the concept of a new test, as well as numerical models and a sensitivity analysis of the modified BCT to the basic geometrical dimensions of the tray. The conducted research clearly shows that the proposed configuration of the load-bearing capacity test of a tray is closer to the actual operation of the packaging. As a result, most of the parameters that are not active under the conditions of the classical BCT become more important in the new configuration, which corresponds to the observations on the real performance of the packaging.

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

Gajewski,

Grabski,

Cornaggia

et al. 2023

Packag Technol Sci

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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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Sensitivity Analysis of Open-Top Cartons in Terms of Compressive Strength Capacity

Cited by 7 publications

References 23 publications

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

Modified Compression Test of Corrugated Board Fruit Tray: Numerical Modeling and Global Sensitivity Analysis

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

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