Advanced surface roughness characterization using 3D scanning technologies and YOLOv4

Abstract

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.