Version 1
: Received: 28 January 2021 / Approved: 28 January 2021 / Online: 28 January 2021 (22:22:21 CET)
How to cite:
Richter, B.; Blanke, N.; Werner, C.; Vollertsen, F.; Pfefferkorn, F. Roughness Parameters for Classification of As-Built AM Surfaces. Preprints2021, 2021010596. https://doi.org/10.20944/preprints202101.0596.v1
Richter, B.; Blanke, N.; Werner, C.; Vollertsen, F.; Pfefferkorn, F. Roughness Parameters for Classification of As-Built AM Surfaces. Preprints 2021, 2021010596. https://doi.org/10.20944/preprints202101.0596.v1
Richter, B.; Blanke, N.; Werner, C.; Vollertsen, F.; Pfefferkorn, F. Roughness Parameters for Classification of As-Built AM Surfaces. Preprints2021, 2021010596. https://doi.org/10.20944/preprints202101.0596.v1
APA Style
Richter, B., Blanke, N., Werner, C., Vollertsen, F., & Pfefferkorn, F. (2021). Roughness Parameters for Classification of As-Built AM Surfaces. Preprints. https://doi.org/10.20944/preprints202101.0596.v1
Chicago/Turabian Style
Richter, B., F. Vollertsen and F. Pfefferkorn. 2021 "Roughness Parameters for Classification of As-Built AM Surfaces" Preprints. https://doi.org/10.20944/preprints202101.0596.v1
Abstract
One of the challenges facing the industrial adoption of additively manufactured parts is the surface roughness on the as-built part. The surface roughness of parts is frequently characterized by metrics specified by international standards organizations. However, these standards list many surface metrics that can make it unclear which to use to best describe the surface. In this work, the ability of the various surface metrics to successfully classify the as-built and post-processed surfaces is studied using linear classification models. Laser polishing via remelting and manual grinding are the post-processing techniques used to smooth the as-built surface. The ability of the linear classifier to successfully categorize the various surfaces is demonstrated, and the various surface metrics are ranked according to the strength of their individual ability to classify the surfaces. This work promotes the method as a potential way to autonomously classify as-built and laser polished surfaces.
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.
