Additive Manufacturing Benchmark 2022 Subcontinuum Mesoscale Tensile Challenge (CHAL-AMB2022-04-MeTT) and Summary of...

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Modeling

Background:

• Additive manufacturing, specifically laser powder bed fusion (L-PBF), produces complex microstructures in materials like nickel alloy IN625.
• Predicting the mechanical performance of L-PBF components is crucial for reliable application but is challenging due to microstructural heterogeneities.
• A benchmarking challenge was established to assess the predictive capabilities of computational models for L-PBF materials.

Purpose of the Study:

• To evaluate the accuracy of various modeling approaches in predicting the stress-strain behavior of meso-scale L-PBF IN625 specimens.
• To assess the ability of models to predict fracture location and pathway in these specimens.
• To identify areas for improvement in material modeling for additive manufacturing.

Main Methods:

• A benchmarking challenge was posed to the modeling community to predict the mechanical response of an IN625 specimen fabricated by L-PBF.
• Specimen characterization included chemical composition, grain structure (EBSD, SEM), and pore structure (X-ray computed tomography).
• Uniaxial tension tests were conducted under displacement control with non-contact strain measurement (DIC), and predictions were compared against experimental data.

Main Results:

• Six different modeling groups submitted predictions for stress-strain behavior, fracture location, and pathway.
• Model predictions showed varied accuracy; elastic modulus and strain at ultimate tensile strength were consistently over-predicted.
• No single model demonstrated superior performance across all prediction tasks, particularly for failure-related properties.

Conclusions:

• Predicting the mechanical behavior and fracture of L-PBF materials remains a significant challenge for current computational models.
• Sub-continuum grain structures and heterogeneous pore distribution in L-PBF components contribute to prediction difficulties.
• Future research should focus on enhancing model maturity and reducing measurement uncertainty to improve predictions for additive manufactured materials.