Rogalka, M.; Grabski, J.K.; Garbowski, T. Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms. Sensors2024, 24, 1772.
Rogalka, M.; Grabski, J.K.; Garbowski, T. Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms. Sensors 2024, 24, 1772.
Rogalka, M.; Grabski, J.K.; Garbowski, T. Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms. Sensors2024, 24, 1772.
Rogalka, M.; Grabski, J.K.; Garbowski, T. Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms. Sensors 2024, 24, 1772.
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 liners, enhances these properties. This study introduces a novel approach to analyze 5-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring layer and overall board thickness, flute height, and center lines of each layer. Integrating 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 faced limitations only 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 to enhance robustness and accuracy.
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.
