Rogalka, M.; Grabski, J.K.; Garbowski, T. Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm. Sensors2023, 23, 6242.
Rogalka, M.; Grabski, J.K.; Garbowski, T. Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm. Sensors 2023, 23, 6242.
Rogalka, M.; Grabski, J.K.; Garbowski, T. Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm. Sensors2023, 23, 6242.
Rogalka, M.; Grabski, J.K.; Garbowski, T. Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm. Sensors 2023, 23, 6242.
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 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 analysis geometric features of corrugated boards based on the images. It allows for precise determination of the geometry of the corrugated layer, as well as the thickness of individual papers in all cardboard layers. 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 evolutionary algorithms. This allows to quickly find the main geometric characteristics of the corrugated board, which in turn leads to the possibility of performing numerical analyzes taking into account the real model of the corrugated board. Calculations made on a real (correctly mapped geometrically) model allow, for example, a more reliable analysis of the composition of the cardboard, and thus also to obtain greater savings in the optimization process.
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.
