Search Results (57)

Modern packaging must be efficient, safe, and attractively designed, while also minimizing unnecessary waste. Eco-design principles, such as material reduction, reusability, and minimal environmental impact, were central to this study. We applied these principles to the development of innovative, multipurpose gift packaging and labels, optimizing material use, eliminating unnecessary printing, and integrating sustainable features such as a structural design which requires no gluing. Alongside choosing eco-friendly materials, namely corrugated cardboard and a biodegradable paper label, eco-design guidelines in packaging and label creation were followed. Packaging of unconventional shape without printing and different versions of labels for bottles and packaging were designed. Graphic elements included on the labels are the logo, illustration, 18th Century font, and lines of varying thicknesses. To provide additional information and enhance product appeal while reducing printing, an interactive element was incorporated. In the conducted study, the respondents of a survey and focus groups evaluated the quality, price range, and visual appeal of packaging and labels. For the augmented reality application, a label with a QR code was created. A scenario and a visual story board were created, and an animation activated via the QR code was produced. The usage experience was tested by the focus groups, who provided feedback on the animation and the overall experience. This iterative process ensured that the packaging and labels met both functional and experiential expectations. Full article

In the packaging industry, corrugated boards are widely used due to many factors like biodegradability, a high strength-to-weight ratio, and also ease of manufacturing. In this study, the finite element analysis of corrugated cardboards under the flat compression test was performed using the open source FEA software Salome-meca. A corrugated board consists of a flute sandwiched between a top and bottom liner. This study was performed with the help of Python scripting in order to iteratively perform many studies by varying the geometric shape of the flute. The pressure distribution along the top and the bottom liner was analyzed. The load–deflection curve for the corrugated cardboard was also analyzed as a part of this study. The boundary condition and the loading condition were chosen in such a way as to correctly represent the situation in real life using the flat crush test in the lab. The contact zone was identified a priori and defined during the preparation of the study. Finally, Code-Aster (the solver utilized by Salome-Meca) was used to solve the finite element solution to the problem. Full article

This study explores the potential of using underutilized materials from agricultural and forestry systems, such as rice husk, wheat straw, and wood strands, in developing corrugated core sandwich panels as a structural building material. By leveraging the unique properties of these biobased materials within a corrugated geometry, the research presents a novel approach to enhancing the structural performance of such underutilized biobased materials. These biobased materials were used in different lengths to consider the manufacturing feasibility of corrugated panels and the effect of fiber length on their structural performance. The average lengths for wood strands and wheat straws were 12–15 cm and 3–7.5 cm, respectively, while rice husks were like particles, about 7 mm long. Due to the high silica content in rice husk and wheat straw, which negatively impacts the bonding performance, polymeric diphenylmethane diisocyanate (pMDI), an effective adhesive for such materials, was used for the fabrication of corrugated panels. Wood strands and phenol formaldehyde (PF) adhesive were used to fabricate flat outer layers. Flat panels were bonded to both sides of the corrugated panels using a polyurethane adhesive to develop corrugated core sandwich panels. Four-point bending tests were conducted to evaluate the panel’s bending stiffness, load-carrying capacity, and failure modes. Results demonstrated that sandwich panels with wood strand corrugated cores exhibited the highest bending stiffness and load-bearing capacity, while those with wheat straw corrugated cores performed similarly. Rice husk corrugated core sandwich panels showed the lowest mechanical performance compared to other sandwich panels. Considering the applications of these sandwich panels as floor, wall, and roof sheathing, all these panels exhibited superior bending performance compared to 11.2 mm- and 17.42 mm-thick commercial OSB (oriented strand board) panels, which are commonly used as building materials. These sandwich structures supported a longer span than commercial OSB panels while satisfying the deflection limit of L/360. The findings suggest the transformative potential of converting renewable yet underutilized materials into an engineered concept, corrugated geometry, leading to the development of high-performance, carbon-negative building materials suitable for flooring and roof applications. Full article

This research explores how temperature and relative humidity impact the mechanical properties of corrugated cardboard. Samples were treated under a range of controlled climate conditions in a climate chamber to simulate varying environmental exposures. Following this conditioning, we performed a series of mechanical tests: the Edge Crush Test (ECT) to assess compressive strength, four-point Bending Tests (BNTs) in both the Machine (MD) and Cross Directions (CD) to evaluate bending stiffness, Sample Torsion Tests (SSTs) for shear stiffness, and Transverse Shear Tests (TSTs) to measure torsional rigidity. By comparing results across these tests, we aim to determine which mechanical property shows the highest sensitivity to changes in humidity levels. Findings from this study are expected to offer valuable insights into the environmental adaptability of corrugated board, particularly for applications in packaging and storage, where climate variability can affect material performance and durability. Such insights will support the development of more robust and adaptable packaging solutions optimised for specific climate conditions. Full article

This article presents an extended numerical approach for evaluating the dynamic response of corrugated cardboard transport packaging under simulated transport conditions. Building upon a simplified method previously introduced, this study integrates a more comprehensive Finite Element Analysis (FEA) framework to capture the non-linear behaviour of packaging subjected to vertical random vibrations. The proposed model employs dynamic, modal, and contact analyses to simulate the deformation of packaging and subsequent strength reduction over multiple impact cycles, reflecting real-world conditions more accurately. The developed approach gives detailed insights into the structural degradation of packaging due to repetitive transport loads and validates the findings through comparative compression tests. The results show that enhanced numerical methods improve the accuracy of load-bearing predictions, thereby supporting optimisation in packaging design for various geometries and transport scenarios. This method offers a valuable tool for evaluating the sustainability and cost-effectiveness of packaging solutions in logistics. Full article

This paper proposes a novel approach to represent the geometry of the corrugated board profile during compression using graphs. Graphs are lighter than images, and the computational time of compression analysis is then significantly reduced compared to using the original image data for the same analysis. The main goal of using such graphs is to gain more knowledge about the mechanical behavior of corrugated boards under compression compared to the current load–deformation curve approach. A node tracking algorithm is applied to characterize the different phases occurring during the compression test in order to predict physical phenomena, including buckling and contact. The main results show that analyzing the nodes provides significant insights into the compression phases, which has not been achieved in the current state of the art. The authors believe that the objective of this research is crucial to better understanding the physics of corrugated boards under compression, and it can also be extended to other engineering structures. Full article

Detecting track geometry and rail surface defects using on-board vehicle monitoring systems is a key issue for rail infrastructure managers to increase availability and reliability while reducing the costs associated with monitoring and maintenance. Rail corrugation is one of the most common rail surface defects which grows in almost all metro, conventional and high-speed lines. This paper focuses on the development of a methodology to detect rail corrugation using axle box acceleration measurements acquired on an in-service high-speed vehicle. The main purpose of the proposed methodology is to distinguish the effect of rail corrugation on the accelerations from the other excitations that can be observed in the same wavelength range. For this purpose, the accelerations are analysed by calculating the fast Fourier transform and the spectrogram. Based on the characteristics of each excitation, the effects of modes of vibration, resonances, bridges, switches, and wheel defects are identified. From the remaining effects, which have congruent characteristics, a hypothesis of rail corrugation is formulated. The hypothesis is consolidated with multibody dynamics simulations and by comparing the corrugation indicators provided by the railway infrastructure company. Full article

Numerical modelling of corrugated paperboard is quite challenging due to its waved geometry and material non-linearity which is affected by the material properties of the individual paper sheets. Because of the complex geometry and material behaviour of the board, there is still scope to enhance the accuracy of current modelling techniques as well as gain a better understanding of the structural performance of corrugated paperboard packaging for improved packaging design. In this study, four-point bending tests were carried out to determine the bending stiffness of un-creased samples in the machine direction (MD) and cross direction (CD). Bending tests were also carried out on creased samples with the fluting oriented in the CD with the crease at the centre. Inverse analysis was applied using the results from the bending tests to determine the material properties that accurately predict the bending stiffness of the horizontal creases, vertical creases, and panels of a box under compression loading. The finite element model of the box was divided into three sections, the horizontal creases, vertical creases, and the box panels. Each of these sections is described using different material properties. The box edges/corners are described using the optimal material properties from bending and compression tests conducted on creased samples, while the box panels are described using the optimal material properties obtained from four-point bending tests conducted on samples without creases. A homogenised finite element (FE) model of a box was simulated using the obtained material properties and validated using experimental results. The developed FE model accurately predicted the failure load of a corrugated paperboard box under compression with a variation of 0.1% when compared to the experimental results. Full article

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways. Full article

Intake is not only the main air supply component of an aircraft, but also one of the forward radar scattering sources. The aerodynamic and stealth performance of intake is critical to the serviceability of advanced fighter aircrafts. The effects of baffle boards with different configurations on the performance of the caret intake with a double S-duct diffuser are presented in this article. The multi-level fast multipole method (MLFMM) and the SST k-ω turbulence model were respectively used to calculate the surface current and the flow field. It was found that the average RCS value of intake can be effectively reduced by installing the baffle board with vertical orientation in the front diffuser, with the DC60 value and the loss of outlet total pressure both increased slightly. The boundary layer separation and the RCS characteristics of intake were closely related to the configuration of the corrugated baffle board. Compared with the traditional curved board, by installing the corrugated board with optimized corrugation number and shape, the stealth performance of intake can be further improved, and the loss of aerodynamic performance can be also reduced. Full article

Corrugated board, widely used in the packing industry, is a recyclable and durable material. Its strength and cushioning, influenced by geometry, environmental conditions like humidity and temperature, and paper quality, make it versatile. Double-walled (or five-ply) corrugated board, comprising two flutes and three liners, enhances these properties. This study introduces a novel approach to analyze five-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring the layer and overall board thickness, flute height, and center lines of each layer. Through the integration of image processing and genetic algorithms, the research successfully developed an algorithm for precise geometric feature identification of double-walled boards. Images were recorded using a special device with a sophisticated camera and image sensor for detailed corrugated board cross-sections. Demonstrating high accuracy, the method only faced limitations with very deformed or damaged samples. This research contributes significantly to quality control in the packaging industry and paves the way for further automated material analysis using advanced machine learning and image sensors. It emphasizes the importance of sample quality and suggests areas for algorithm refinement in order to enhance robustness and accuracy. Full article

The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of layers, and environmental conditions such as humidity and temperature. This study introduces an innovative application of convolutional neural networks (CNNs) for analyzing and classifying images of corrugated boards, particularly those with deformations. For this purpose, a special device with advanced imaging capabilities, including a high-resolution camera and image sensor, was developed and used to acquire detailed cross-section images of the corrugated boards. The samples of seven types of corrugated board were studied. The proposed approach involves optimizing CNNs to enhance their classification performance. Despite challenges posed by deformed samples, the methodology demonstrates high accuracy in most cases, though a few samples posed recognition difficulties. The findings of this research are significant for the packaging industry, offering a sophisticated method for quality control and defect detection in corrugated board production. The best classification accuracy obtained achieved more than 99%. This could lead to improved product quality and reduced waste. Additionally, this study paves the way for future research on applying machine learning for material quality assessment, which could have broader implications beyond the packaging sector. Full article

In this study, a numerical investigation was conducted on a verified packaging model, which sticks out beyond the pallet base area, which will evidently weaken its load-bearing capacity. This could lead to damage of the protected goods transported within this packaging. It might also result in the unnecessary overengineered design of the packaging, particularly when the potential for overhanging is anticipated beforehand, but its exact extent is not known. The article analyzed hundreds of cases, varying in terms of packaging dimensions (from 150 mm up to 600 mm), the extent of protrusion beyond the edge of the pallet (from 1% to 50% of box dimensions) and the use of various corrugated boards (B-, C-, EB- and BC-flute), in order to assess the decrease in the load-bearing capacity of the packaging compared to reference packaging, which was not overhanging on a pallet. For instance, it appeared that the decrease in the load-bearing capacity of the packaging when overhanging was insensitive to the corrugated cardboard material used. Additionally, the decrease in box strength was rapid while overhanging, even for a small value of overhanging. Full article

Corrugated board is an environmentally friendly, commonly used packing material. Its basic structure consists of two liners and a flute between them. The mechanical properties and strength of the corrugated board depend not only on the constituent papers but also its geometry, which can be distorted, however, due to various factors related to its manufacturing process or use. The greatest distortion occurs in the corrugated layer, which, due to crushing, significantly deteriorates the functional properties of cardboard. In this work, two algorithms for the automatic classification of corrugated board types based on images of deformed corrugated boards using artificial intelligence methods are presented. A prototype of a corrugated board sample image acquisition device was designed and manufactured. It allowed for the collection of an extensive database of images with corrugated board cross-sections of various types. Based on this database, two approaches for processing and classifying them were developed. The first method is based on the identification of the geometric parameters of the corrugated board cross-section using a genetic algorithm. After this stage, a simple feedforward neural network was applied to classify the corrugated board type correctly. In the second approach, the use of a convolutional neural network for corrugated board cross-section classification was proposed. The results obtained using both methods were compared, and the influence of various imperfections in the corrugated board cross-section was examined. Full article

This paper presents a comprehensive review of optimization practices in the corrugated board industry, which has recently experienced significant interest in using optimization methodologies driven by sustainable demands and increasing computational capabilities. The authors cover different review perspectives, including historical context, manufacturing applications, design optimization, and numerical optimization algorithms used. The main findings of this study indicate that the corrugated board industry has experienced a shift from trial-and-error and expert-driven approaches to data-centric strategies, particularly since the beginning of the 21st century. Interestingly, the industry has also adopted Multi-Disciplinary Optimization techniques from other fields, which demonstrates the importance of knowledge convergence across sectors. However, due to the complex nature of corrugated boards—including materials, design, and manufacturing processes—there is still much research to be done in this area. This work provides guidance for future research directions and encourages innovation and improvement in corrugated board optimization practices. In particular, the strong developments of material models for paper in recent years will boost the use of optimization tools in this field. Full article