The surface characterization of materials after Robot Laser Hardening (RLH) is a technically demanding procedure. RLH is commonly used to harden parts, especially when subject to wear. By changing their surface properties, this treatment can offer several benefits such as lower costs for additional machining, no use of cooling agents or chemicals, high flexibility, local hardening, minimal deformation, high accuracy, and automated and integrated process in the production process. However, the surface roughness strongly depends on the heat treatment and parameters used in the process. This article used a network theory approach (i.e., the visibility network in 2D space) to analyze the surface roughness of tool steel EN100083-1 upon RLH. Specifically, two intelligent methods were merged in this investigation. Firstly, a genetic algorithm was applied to derive a relationship between the parameters of the robot laser cell and topological surface properties. Furthermore, convolutional neural networks allowed the assessment of surface roughness based on 2D photographic images
Babic M., Wangyao P., Ster B., Marinkovic D., Fragassa C. (2022). Modelling the Surface Roughness of Steel after Laser hardening by using 2D Visibility Network, Convolutional neural Networks and Genetic Programming. FME TRANSACTIONS, 50(3), 393-402 [10.5937/fme2203393B].
Modelling the Surface Roughness of Steel after Laser hardening by using 2D Visibility Network, Convolutional neural Networks and Genetic Programming
Marinkovic D.;Fragassa C.
2022
Abstract
The surface characterization of materials after Robot Laser Hardening (RLH) is a technically demanding procedure. RLH is commonly used to harden parts, especially when subject to wear. By changing their surface properties, this treatment can offer several benefits such as lower costs for additional machining, no use of cooling agents or chemicals, high flexibility, local hardening, minimal deformation, high accuracy, and automated and integrated process in the production process. However, the surface roughness strongly depends on the heat treatment and parameters used in the process. This article used a network theory approach (i.e., the visibility network in 2D space) to analyze the surface roughness of tool steel EN100083-1 upon RLH. Specifically, two intelligent methods were merged in this investigation. Firstly, a genetic algorithm was applied to derive a relationship between the parameters of the robot laser cell and topological surface properties. Furthermore, convolutional neural networks allowed the assessment of surface roughness based on 2D photographic imagesFile | Dimensione | Formato | |
---|---|---|---|
219_FME, Modelling the surface roughness.pdf
accesso aperto
Tipo:
Versione (PDF) editoriale
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione
2.51 MB
Formato
Adobe PDF
|
2.51 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.