About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Additive Manufacturing for Energy Applications IV
|
Presentation Title |
Data-driven Modeling for Microstructure-property Relationships of 17-4 Stainless Steel |
Author(s) |
Michael Porro, Bin Zhang, Akanksha Parmar, Yung C. Shin |
On-Site Speaker (Planned) |
Yung C. Shin |
Abstract Scope |
The high upfront cost of determining the resultant mechanical properties of metal parts built by additive manufacturing significantly hinders widespread adoption in the industry. The objective of this study is to establish a procedure to reduce this cost by using data-driven modeling to predict the mechanical properties of AM-built 17-4 PH stainless steel parts. A data-driven model can provide a quicker way of predicting mechanical properties based on given microstructure information. To build a data-driven model the widely available but scattered data in literature are collected and utilized, and additional tensile specimens were built by binderjet and laser powderbed fusion processes, and resultant microstructure and mechanical properties were experimentally measured. The collected data from the literature and experiments were used to build data-driven models of structure-property relationships. The established data-driven models exhibited good prediction capabilities for yield strength, ductility and ultimate tensile strength for a number of validation cases. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, Mechanical Properties |