Advanced surface roughness characterization using 3D scanning technologies and YOLOv4

In modern manufacturing, providing high-quality surface finishes to mechanical parts is critical to maintaining product integrity and optimizing the performance of mechanical systems. Surface roughness directly affects various aspects of part functionality, including friction, wear resistance, and o...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:E3S web of conferences 2024, Vol.525, p.5014
Hauptverfasser: Karimova, Nazokat, Ochilov, Ulugbek, Tuyboyov, Oybek, Yakhshiev, Sherali, Egamberdiev, Ilhom
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In modern manufacturing, providing high-quality surface finishes to mechanical parts is critical to maintaining product integrity and optimizing the performance of mechanical systems. Surface roughness directly affects various aspects of part functionality, including friction, wear resistance, and overall durability. Therefore, accurate and efficient assessment of surface finish quality is of paramount importance to ensure the reliability and longevity of mechanical components. To meet this need, this study proposes an intelligent system that leverages the capabilities of deep learning and computer vision technologies to estimate the surface roughness of machined steel parts. By combining these advanced techniques, manufacturers can automate and improve the surface quality inspection process, resulting in increased productivity and reduced costs associated with manual inspection methods. This paper proposes an innovative method for determining surface roughness after machining by combining 3D scanning technologies with the deep learning algorithm YOLOv4.
ISSN:2267-1242
2267-1242
DOI:10.1051/e3sconf/202452505014