Advancing digital twins of wire arc-DED through process control with a multi-modal sensor array

Author

Jeffery Logan Betts, Mississippi State UniversityFollow

ORCID

https://orcid.org/0000-0002-2091-5094

Issuing Body

Mississippi State University

Advisor

Priddy, Matthew W.

Committee Member

Dantin, Matthew J.

Committee Member

Barrett, Christopher D.

Committee Member

Dickel, Doyl E.

Date of Degree

12-13-2024

Original embargo terms

Complete embargo 2 years

Document Type

Dissertation - Open Access

Major

Engineering (Mechanical)

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Michael W. Hall School of Mechanical Engineering

Abstract

The goal of this work was to advance digital twins of wire-arc directed energy deposition (arc-DED) through process control with a multi-modal sensor array. Digital twins allow for synchronization, context, and visualization of in situ data. Additionally digital twins allow for bi-directional communication between the virtual and physical system, allowing for process control.This work implemented feedback control of the contact tip to work piece distance (CTWD), established a standard method to create modular unified robot description format (URDF) of the arc-DED system, and produced a complex component while collecting multi-modal data within the digital twin for robotic additive welding (DRAW) powered by Robot Operating System 2 (ROS2). This work also developed a robust pre- and post-processing framework in Python for three-dimensional FE thermal models of arc-DED. The framework looked at the effects of processing parameters and model convergence, through a full factorial design of experiments. Controlling CTWD is critical for wire-arc DED, and this work implemented a novel method of feedback control that can measure the weld bead height in situ. It is imperative for digital twins to have a comprehensive and accurate virtual representation. The standard method to create a URDF for arc-DED outlined in this work includes the entire robotic welding cell, all robotic axes, and sensors in an accurate, and modular framework. Following this development a large complex geometry (42.78 lb) using arc-DED, and DRAW reliably captured approximately 200 Gb of multi-modal data over the twenty-two hours of manufacturing time. This demonstrated the ability to produce a forty-two-pound complex component, four times larger than the next largest component produced on this system prior. Finally, this demonstrated the reliability of utilizing ROS 2 for multi-modal data capture and process control.

Recommended Citation

Betts, Jeffery Logan, "Advancing digital twins of wire arc-DED through process control with a multi-modal sensor array" (2024). Theses and Dissertations. 6397.
https://scholarsjunction.msstate.edu/td/6397