A computational method for detecting aspect ratio and problematic features in additive manufacturing

Ruihuan Ge, Joseph Flynn

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)

Abstract

In metal additive manufacturing, geometries with high aspect ratio (AR) features are often associated with defects caused by thermal stresses and other related build failures. Ideally, excessively high AR features would be detected and removed in the design phase to avoid unwanted failure during manufacture. However, AR is scale and orientation independent and identifying features across all scales and orientations is exceptionally challenging. Furthermore, not all high AR features are as easy to recognise as thin walls and fine needles. There is therefore a pressing need for further development in the field of problematic features detection for additive manufacturing processes. In this work, a dimensionless ratio (D1/D2) based on two distance metrics that are extracted from triangulated mesh geometries is proposed. Based on this method, geometries with different features (e.g. thin wall, helices and polyhedra) were generated and evaluated to produce metrics that are similar to AR. The prediction results are compared with known theoretical AR values of typical geometries.By combining this metric with mesh segmentation, this method was further extended to analyse the geometry with complex features. The proposed method provides a powerful, general and promising way to automatically detect high AR features and tackle the relevant defect issues prior to manufacture.

Original languageEnglish
Pages (from-to)519-535
Number of pages17
JournalJournal of Intelligent Manufacturing
Volume33
Issue number2
Early online date10 Nov 2021
DOIs
Publication statusPublished - 28 Feb 2022

Keywords

  • Additive manufacturing (AM)
  • Aspect ratio (AR)
  • Mesh processing
  • Thermal stress

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A computational method for detecting aspect ratio and problematic features in additive manufacturing'. Together they form a unique fingerprint.

Cite this