A Surface Roughness Characterization Method for Additively Manufactured Products

Andi Wang, Davoud Jafari, Tom H.J. Vaneker, Qiang Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

In many additive manufacturing processes, surface roughness is a critical quality concern. Due to the nature of the layer-by-layer manufacturing process, the pattern of surface roughness depends on the location on the surface, i.e., the layer number and the location within each layer. Adequate description of the surface roughness enables us to develop effective postprocessing plans, reveal the root causes of the roughness, and generate accurate compensation schemes. In this work, we propose a three-step surface roughness characterization method (SRCM). This method is based on the dense point cloud data generated from the surface scan of additively manufactured products. First, we use a double kernel smoothing spatial variogram estimator to represent the heterogeneous roughness property at different surface locations. Second, we extract the magnitude and scale of surface roughness from the estimated variogram. Third, we use Gaussian Process to build a roughness map on the entire surface based on the roughness characterization on these sampled points. The SRCM is demonstrated from a high-density 3D scan of a cylindrical product fabricated by a wire-arc additive manufacturing process. It shows that our approach serves as an effective tool to infer the roughness map from the 3D point cloud data. In the end, we will briefly discuss how to use the inferred roughness map to develop an optimal surface smoothing method.

Original languageEnglish
Title of host publicationProceedings of ASME 2022 17th International Manufacturing Science and Engineering Conference (MSEC2022)
Subtitle of host publicationVol. 1: Additive manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing
PublisherAmerican Society of Mechanical Engineers
ChapterMSEC2022-85691
Number of pages7
Volume1
ISBN (Electronic)978-0-7918-8580-2
DOIs
Publication statusPublished - 30 Sept 2022
EventASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022 - West Lafayette, United States
Duration: 27 Jun 20221 Jul 2022

Conference

ConferenceASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022
Abbreviated titleMSEC2022
Country/TerritoryUnited States
CityWest Lafayette
Period27/06/221/07/22

Keywords

  • NLA
  • additive manufacturing
  • point cloud data
  • surface roughness
  • 3D scanning

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