About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Interrelated Process-Geometry-Microstructure Relationships for Wire-feed Laser Additive Manufacturing |
Author(s) |
Sen Liu, Craig Brice, Xiaoli Zhang |
On-Site Speaker (Planned) |
Sen Liu |
Abstract Scope |
Wire-feed laser additive manufacturing has gained attention for years with its promises of high-level automation, reducing materials waste, overall costs, and large-scale volume production. However, printing material with multiple desired bead properties is a great challenge due to the high cost of experiments and the interdependent correlations among the properties. This study proposes a comprehensive multi-property integrated design framework based on machine learning from experiments data to enable quality control of part surface quality, geometry and microstructural characteristics. The printed bead process – microstructure – geometry relationships and process features' importance are investigated. The overall bead quality (smooth, ripple, and failed), geometry (e.g. bead height, width, fusion zone depth, fusion zone area), and microstructures are simultaneously optimized to obtain the optimal processing window. The validation results highlight that the process parameterizations by this framework can speed up the manufacturing of materials with the desired bead multi-performance. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, Characterization |