Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs

TL;DR

This paper introduces neural ODE-based surrogate models for real-time prediction of residual stresses and temperature fields in DED additive manufacturing, enabling digital twins and process optimization.

Contribution

It presents a novel neural ODE approach for fast, accurate surrogate modeling of residual stresses in DED additive manufacturing processes.

Findings

Surrogate models predict temperature and stress with high accuracy.

Models operate faster than real-time, enabling real-time process control.

Facilitates on-the-fly re-optimization of manufacturing processes.

Abstract

A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifecycle. For Laser Directed Energy Deposition of Wire (DED-LB/w) additive manufacturing processes, digital twins may help to control the residual stress design in build parts. This study focuses on providing faster-than-real-time and highly accurate surrogate models for the formation of residual stresses by employing neural ordinary differential equations. The approach enables accurate prediction of temperatures and altered structural properties like stress tensor components. The developed surrogates can ultimately facilitate on-the-fly re-optimization of the ongoing manufacturing process to achieve desired structural outcomes. Consequently, this building block contributes significantly to realizing digital twins and the first-time-right paradigm in additive manufacturing.