Reliability of metal additive manufactured materials from modeling the microstructure at different length scales

Brian Snider-Simon, George Frantziskonis

Research output: Contribution to journalArticlepeer-review

Abstract

As metal additive manufacturing transitions from a prototypical manufacturing process to producing in-service parts, there is an urgent need for methodologies that are capable of predicting material response and reliability. There is a large body of research that examines physics-based methodologies and, more recently, data-driven models to simulate the relationships surrounding the process–structure–properties paradigm of additive manufactured materials. Both approaches share the same objective; namely, for a given set of process parameters, predict the resulting micro-structure and/or mechanical response of the printed material. Physics-based methodologies come with a high computational cost as the physical phenomena behind the manufacturing process spans multiple length and time scales. Conversely, data-driven models have a low computational cost at the expense of by-passing the physical mechanisms and instead finding correlation between diverse input and output data sets. As a result, the interpretation of data-driven models can be difficult and they have the potential to be bias and brittle. This paper presents a novel workflow that takes into consideration process parameters, build strategy and data obtained from standard material science tests to develop realistic, statistical, finite element models of material micro-structure that can be used to predict reliability without the need to explicitly model the physics behind the manufacturing process. Using micro-structural information extracted from as-fabricated material builds, we show that porosity can be modeled using marked spatial point patterns and scan track morphology can be idealized as concentric, multi-phase, half cylindrical volumes assembled according to the build strategy specified for the material. Furthermore, we show that the finite element model can be tuned to experimental test data, consistent with the concurrent or top-down approach in virtual materials testing. To illustrate this process, we apply the workflow to data obtained from various sources in the literature for as-fabricated AlSi10Mg manufactured using selective laser melting (SLM), a powder bed additive manufacturing process.

Original languageEnglish (US)
Article number102629
JournalAdditive Manufacturing
Volume51
DOIs
StatePublished - Mar 2022
Externally publishedYes

Keywords

  • Additive manufacturing
  • Continuum damage mechanics
  • Multi-objective optimization
  • Multi-scale analysis
  • Neural networks
  • Non-linear finite elements
  • Process–microstructure–property relationship

ASJC Scopus subject areas

  • Biomedical Engineering
  • Materials Science(all)
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering

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