About this Abstract |
Meeting |
MS&T21: Materials Science & Technology
|
Symposium
|
Additive Manufacturing: Advanced Characterization for Industrial Applications
|
Presentation Title |
Predicting Failure Location in Additively Manufactured Metals Using an Improved Void Descriptor Function |
Author(s) |
Dillon Watring, Jake Benzing, Orion Kafka, Newell Moser, Li-Anne Liew, John Erickson, Nikolas Hrabe, Ashley Spear |
On-Site Speaker (Planned) |
Dillon Watring |
Abstract Scope |
Metal additive manufacturing (AM) has become a vital tool in many industries. However, large variations in pore distributions (size, shape, and location) are a major concern in structural applications given certain porosity populations can cause premature failure in metal components. Parametric studies show that the variations in porosity depend on AM processing parameters. A previously derived pore metric called the void descriptor function was shown to improve the predictive capabilities of fracture location by accounting for pore location, size (assuming spherical shape), and distance to free surface. This work expands upon the original void descriptor function to generalize and enhance the representation of pore networks by fitting ellipsoids around 3D pores and by weighting pore location based on neighbors of interest, which more accurately represents pore clustering. This improved and computationally efficient function is shown to enable accurate prediction of fracture location in mesoscale additively manufactured Inconel 718 tensile specimens. |