A machining digital twin for hybrid manufacturing

Jake Dvorak, Aaron Cornelius, Greg Corson, Ross Zameroski, Leah Jacobs, Joshua Penney, Tony Schmitz

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Hybrid manufacturing consisting of metal additively manufactured preforms and computer numerical control (CNC) machining has been established to be an effective method for high material use rates. However, hybrid manufacturing introduces unique challenges. Near-net shape designs are typically selected, which result in a smaller margin for part placement within the stock and stringent requirements for work coordinate system identification. Additionally, less stock material reduces the preform stiffness, which limits the material removal rates during machining. This paper demonstrates a digital twin for CNC machining of a wire arc additively manufactured preform that implements: 1) structured light scanning for stock model identification and tool path generation; 2) a fused filament fabrication apparatus to attach temporary fiducials and scan targets to the preform that enable coordinate system definition for both the CAM and CNC machine; 3) preform and tool tip frequency response function measurements to enable stable milling parameter selection; and 4) post-manufacturing measurements of geometry, surface finish, and structural dynamics to confirm designer intent. These efforts define key components of the machining digital twin for hybrid manufacturing.

Original languageEnglish
Pages (from-to)786-793
Number of pages8
JournalManufacturing Letters
Volume33
DOIs
StatePublished - Sep 2022
Externally publishedYes

Keywords

  • Wire arc additive manufacturing
  • fiducial
  • hybrid manufacturing
  • machining dynamics
  • milling
  • structured light scanning

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