Abstract
The optical perception of high precision, fine grinded surfaces is an important quality feature for these products. Its manufacturing process is rather complex and depends on a variety of process parameters (e.g. feed rate, cutting speed) which have a direct impact on the surface topography.
To improve the conventional methods of condition monitoring, a new image processing analysis approach is needed to get a faster and more cost-effective analysis of produced surfaces. For this reason, different optical techniques based on image analysis have been developed over the past years. Fine grinded surface images have been generated under constant boundary conditions in a test rig built up in a lab.
Within this study the image of each grinded surface is analyzed regarding its measured arithmetic average roughness value (Ra) by the use of random forest algorithms.
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Hinz, M., Pietruschka, J., Bracke, S. (2022). Application of Random Forest Algorithm for the Quality Determination of Manufactured Surfaces. In: Hamrol, A., Grabowska, M., Maletič, D. (eds) Advances in Manufacturing III. MANUFACTURING 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-00218-2_8
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DOI: https://doi.org/10.1007/978-3-031-00218-2_8
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