Topic Editors

Faculty of Environmental and Mechanical Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland
Department of Biosystems Engineering, Poznań University of Life Sciences, Poznan, Poland

Identification of Bio- and Eco-Materials Using Advanced Computational Methods

Abstract submission deadline
closed (30 March 2024)
Manuscript submission deadline
30 May 2024
Viewed by
2249

Topic Information

Dear colleagues,

In the modern world, ‘eco’ and ‘bio’ have become two of the most used prefixes. They identify a given product with a clear trend related to both ecology, closed circuit, and sustainable production, as well as re-use and recycling, or the recently very popular upcycling. On the other hand, tools based on advanced computational methods, i.e., numerical simulations, inverse analysis, artificial intelligence, and machine learning, are increasingly used to assess quality, and there is a search for the trends, recognition, and identification of these products. In addition, new, powerful numerical algorithms and metamodels based on deep learning and stochastic processes allow us to quickly and effectively achieve desired goals. In this topic, we want to collect works related to bio-products and eco-materials, but also bio-materials widely used in orthopedics and more broadly in medicine. The collection of bio- and eco-materials is not only limited to biologically compatible medical implants or modern ecological building materials. They belong to a much wider space, also including all kinds of food, textile, and wood or paper products, as well as waste and their use for the production of green energy and much more.

There are no particular restrictions on the thematic areas of this Special Issue, as long as submissions are related to these kinds of materials, with particular emphasis on the appropriate measurements and experimental techniques used for their identification and characterization. The readers and authors of this SI are encouraged to send their latest research studies in these areas, with an emphasis on experimental validation and empirical evidence as well as metamodels and artificial intelligence in the identification of eco- and bio-materials.

Dr. Tomasz Garbowski
Prof. Dr. Maciej Zaborowicz
Topic Editors

Keywords

  • computational methods
  • inverse analysis
  • artificial intelligence
  • artificial neural networks
  • deep learning
  • Gaussian processes
  • bio-products
  • eco-materials
  • biomaterials
  • identification
  • measurements
  • experimental data

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Materials
materials
3.4 5.2 2008 13.9 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600 Submit

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Published Papers (2 papers)

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20 pages, 22989 KiB  
Article
Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms
by Maciej Rogalka, Jakub Krzysztof Grabski and Tomasz Garbowski
Sensors 2024, 24(6), 1772; https://doi.org/10.3390/s24061772 - 09 Mar 2024
Viewed by 420
Abstract
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 [...] Read more.
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
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17 pages, 14551 KiB  
Article
Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm
by Maciej Rogalka, Jakub Krzysztof Grabski and Tomasz Garbowski
Sensors 2023, 23(13), 6242; https://doi.org/10.3390/s23136242 - 07 Jul 2023
Cited by 3 | Viewed by 1086
Abstract
The corrugated board is a versatile and durable material that is widely used in the packaging industry. Its unique structure provides strength and cushioning, while its recyclability and bio-degradability make it an environmentally friendly option. The strength of the corrugated board depends on [...] Read more.
The corrugated board is a versatile and durable material that is widely used in the packaging industry. Its unique structure provides strength and cushioning, while its recyclability and bio-degradability make it an environmentally friendly option. The strength of the corrugated board depends on many factors, including the type of individual papers on flat and corrugated layers, the geometry of the flute, temperature, humidity, etc. This paper presents a new approach to the analysis of the geometric features of corrugated boards. The experimental set used in the work and the created software are characterized by high reliability and precision of measurement thanks to the use of an identification procedure based on image analysis and a genetic algorithm. In the applied procedure, the thickness of each layer, corrugated cardboard thickness, flute height and center line are calculated. In most cases, the proposed algorithm successfully approximated these parameters. Full article
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