Towards rapid, in situ characterization for materials-on-demand manufacturing

Ashif Sikandar Iquebal, Bhaskar Botcha, Satish Bukkapatnam

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Recent hybrid additive-subtractive manufacturing platforms can usher a materials-on-demand manufacturing paradigm where the materials, along with geometry and surface morphology, are designed to meet the functionality. While in situ measurement systems have advanced to monitor geometric and morphological variations, microstructure and phase characterization methods remain slow, impeding this vision. Towards addressing this issue, we present an approach to characterize the material concurrently during fabrication by associating in situ sensor measurements with the microstructures. Analysis of sensor data from machining of 316L steel suggests that the microstructural variations under different process parameters can be predicted in real time with 93% accuracy.

Original languageEnglish (US)
Pages (from-to)29-33
Number of pages5
JournalManufacturing Letters
Volume23
DOIs
StatePublished - Jan 2020
Externally publishedYes

Keywords

  • High-resolution sensing
  • Hybrid manufacturing
  • Machine learning
  • Machining dynamics
  • Microstructure characterization

ASJC Scopus subject areas

  • Mechanics of Materials
  • Industrial and Manufacturing Engineering

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