Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model...

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Science

Background:

• Additive manufacturing (AM) presents challenges in predicting material behavior due to microstructural variations.
• Accurate prediction of local mechanical responses, such as grain-average elastic strain tensors, is crucial for AM component integrity.
• The Air Force Research Laboratory's challenge provided experimental data and microstructural characterization of IN625 for mechanical response prediction.

Purpose of the Study:

• To develop and present computational strategies for predicting grain-average elastic strain tensors in IN625 during tensile loading.
• To evaluate the effectiveness of a genetic algorithm (GA)-based material model identification method.
• To introduce and assess a proper generalized decomposition (PGD)-based reduced order method for improved material model calibration.

Main Methods:

• Utilized a characterized microstructural image from experimental data.
• Employed a genetic algorithm (GA)-based approach for material model identification during the competition phase.
• Introduced a proper generalized decomposition (PGD)-based reduced order method for enhanced material model calibration in the post-competition phase.

Main Results:

• The GA-based method provided initial predictions for challenge grain mechanical responses.
• The PGD-based method demonstrated efficiency in material model calibration, leading to improved accuracy.
• The developed methods successfully handled large-scale computational problems for local response identification.

Conclusions:

• The presented computational strategies are capable of predicting local mechanical responses in additively manufactured materials.
• The PGD-based reduced order method shows significant promise for efficient and accurate material model calibration.
• The findings contribute to advancing predictive modeling in mechanics and materials science for AM applications.