Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks

Mojtaba Mozaffar, Arindam Paul, Reda Al-Bahrani, Sarah Wolff, Alok Choudhary, Ankit Agrawal, Kornel Ehmann, Jian Cao*

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Directed Energy Deposition (DED) is a growing additive manufacturing technology due to its superior properties such as build flexibility at multiple scales and limited waste. However, both experimental and physics-based models have limitations in providing accurate and computationally efficient predictions of process outcomes, which is essential for real-time process control and optimization. In this work, a recurrent neural network (RNN) structure with a Gated Recurrent Unit (GRU) formulation is proposed for predicting the high-dimensional thermal history in DED processes with variations in geometry, build dimensions, toolpath strategy, laser power and scan speed. Our results indicate that the model can accurately predict the thermal history of any given point of the DED build on a test-set database with limited training. The model's general applicability and ability to accurately predict thermal histories has been demonstrated through two overarching tests conducted for long time spans and non-trained geometries.

Original languageEnglish (US)
Pages (from-to)35-39
Number of pages5
JournalManufacturing Letters
Volume18
DOIs
StatePublished - Oct 2018

Keywords

  • Additive manufacturing
  • Artificial neural networks
  • Deep learning
  • Directed Energy Deposition
  • Process parameters
  • Recurrent neural network

ASJC Scopus subject areas

  • Mechanics of Materials
  • Industrial and Manufacturing Engineering

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