Additive Manufacturing Fatigue and Fracture V: Processing-Structure-Property Investigations and Application to Qualification: Fatigue Modeling and Prediction
Sponsored by: TMS Structural Materials Division, TMS: Additive Manufacturing Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: Nik Hrabe, National Institute of Standards and Technology; John Lewandowski, Case Western Reserve University; Nima Shamsaei, Auburn University; Mohsen Seifi, ASTM International/Case Western Reserve University; Steve Daniewicz, University of Alabama
Wednesday 8:30 AM
March 17, 2021
Room: RM 2
Location: TMS2021 Virtual
Session Chair: Nik Hrabe, National Institute of Standards and Technology (NIST)
8:30 AM Invited
Damage Tolerant Approach in Additively Manufactured Metallic Materials: Mauro Madia1; Uwe Zerbst1; Tiago Werner1; 1Bundesanstalt fuer Materialforschung und -pruefung (BAM)
Damage tolerance counts as one of the most widespread approach to fatigue assessment and surely as one of the most promising in understanding the process-structure-property-performance relationships in additively manufactured metallic materials. Manufacturing defects, surface roughness, microstructural features, short and long crack fatigue propagation, residual stresses and applied loads can be taken into consideration in a fracture mechanics-based fatigue assessment. Many aspects are crucial to the reliable component life prediction. Among those a prominent role is played by an accurate measurement and modelling of the short crack fatigue behavior, and reliable statistical characterization of defects and residual stresses. This work aims at addressing the issues related to both experimental testing, fatigue and fatigue crack propagation, and fracture mechanics-based modelling of fatigue lives. Examples will be provided on an additively manufactured AISI 316 L.
9:00 AM
Defect-based Fatigue Model for AlSi10Mg Produced by Laser Powder Bed Fusion Process: Avinesh Ojha1; Wei-Jen Lai1; Ziang Li1; Carlos Engler-Pinto Jr.1; Xuming Su1; 1Ford Motor Company
Defect is inevitable in metal parts built by laser powder bed fusion (L-PBF) process. The size, shape, and location of the defect play critical roles in determining material’s fatigue strength. Due to the random nature of defect in the part, statistical method must be employed for fatigue strength estimation. A defect-based statistical fatigue strength model has been developed and validated for L-PBF AlSi10Mg containing keyhole defects with different size distributions. Artificial defects were also introduced for model validation. The model modified Murakami’s formulation to address material dependence and followed Romano’s approach to consider the statistical behavior of fatigue strength. However, the proposed model is unable to predict fatigue strength of material containing lack-of-fusion defect possibly due to higher stress concentration induced by its morphology.
9:20 AM
State-of-the-Art in Predicting Fatigue Life for Applications in Metal-based Additive Manufacturing: Newell Moser1; Orion Kafka1; Jake Benzing1; Nicholas Derimow1; Nik Hrabe1; Edward Garboczi1; 1National Institute of Standards and Technology
Based on today’s understanding and process control for metal AM, designers should expect performance aspects to vary from part-to-part and machine-to-machine. Hence, there has not been much success in utilizing AM for safety critical applications. This work aims to highlight the strengths and weaknesses of the current state-of-the-art in predicting fatigue life, while also bearing in mind the unique challenges associated with metal AM. An overview will be given on the difficulties of experimentally characterizing fatigue. Additionally, both analytical and computational approaches towards modeling fatigue life will be discussed, as well as strategies to link experimental data to these types of fatigue life models. Furthermore, we will introduce our current work at NIST to predict fatigue life in metal AM using computational mechanics, which includes a model based on continuum damage mechanics.
9:40 AM Invited
Synergistic Effects of Defects and Microstructure on Fatigue Behavior of LB-PBF Metallic Materials: Shuai Shao1; Nima Shamsaei1; 1Auburn University
This presentation focuses on synergistic effects of volumetric defects and microstructure on fatigue behavior of laser beam powder bed fusion (LB-PBF) fabricated alloys. This synergy is central to address the compromised and/or hard-to-predict structural integrity (such as fatigue performance) of these alloys due to the presence and variability of volumetric defects, including pores and lack-of-fusions, and the surrounding microstructure. To provide understanding on this synergy, a coordinated research effort is carried out integrating systematic fabrication, fatigue experiments, analysis, and linear elastic as well as crystal plasticity finite element (FE) simulations. The experimental effort provides foundational data and initial knowledge on the synergy. Building upon the foundation, parametric FE simulations can then be used to systematically assess the effects of defect size, shape, and surrounding microstructure on the fatigue behavior of LB-PBF metallic materials. The findings can then be used to improve the reliability of defect- and microstructure- sensitive fatigue models.
10:10 AM
Microstructure-based Model Validation and Predictions of Single-build-plate Fatigue Strength Sensitivity for Additively Manufactured Ti-6Al-4V: Orion Kafka1; Newell Moser1; Jake Benzing1; Nicholas Derimow1; Nikolas Hrabe1; Edward Garboczi1; 1NIST
Microstructure-based structure-properties-performance modeling shows promise as a predictive tool for additive manufacturing (AM), where highly variable microstructures (due to processing sensitivity) drive highly variable performance, especially when compared with conventionally processed metals. This work starts by validating the predicted mechanical behavior from a crystal plasticity model implemented within three solution frameworks against electron backscatter diffraction and X-ray computed tomography measurements conducted during and after tensile tests to failure of AM Ti-6Al-4V. The relative fatigue strength of several parts manufactured on the same build plate are predicted, based on observed changes in microstructures and defects. Our results highlight the importance of understanding of the inter-dependency between all relevant variables (like build plate location) and the measurable impact on microstructure and mechanical behavior. Our work shows potential for enabling rapid part design and qualification, by using microstructure-based predictive modeling to help identify and mitigate possible risk factors before conducting a physical build.
10:30 AM
3-D Convolutional Neural Networks for Pore Analysis in Metal Additive Manufacturing Builds: Andrew Kitahara1; Ziheng Wu1; Srujana Yarasi1; Nihal Sivakumar1; Anthony Rollett1; Elizabeth Holm1; 1Carnegie Mellon University
Pore structures are of significant interest in metal additive manufacturing (AM) because of their direct relation to mechanical properties. We extend a 2D powder particle characterization tool for application to 3D AM pores. As-built and powder specimens are imaged in 3-D with CT. The pores are segmented from the bulk material, and a pretrained 3-D convolutional neural network (3DCNN) is used as a feature descriptor for the pores. These pores are then clustered via K-Means into a small number of unique morphological types, which are easily verified by human inspection to have similar appearance, shape, size, morphology, and so on. Individual pores can be classified, using machine learning, as intrinsic, keyhole, or lack of fusion porosity. Further, the distribution of pore types can be associated with build parameters. We demonstrate the methodology of our approach and discuss how this analysis tool fits within the framework of exploring process-structure-property relationships.
10:50 AM
Bayesian Inference of Elastic Constants and Texture Coefficients in Additively Manufactured Alloys Using Resonant Ultrasound Spectroscopy: Jeffrey Rossin1; Patrick Leser2; Chris Torbet1; Stephen Smith2; Samantha Daly1; Tresa Pollock1; 1University of California, Santa Barbara; 2NASA Langley Research Center
Additive manufacturing (AM) has enabled design and processing advantages for metallic components. Despite the advantages of AM, qualification of AM components for critical applications has limited its widespread usage. Further, AM microstructures are typically anisotropic. Resonant ultrasound spectroscopy (RUS) is a useful technique for determining elastic constants using inversion techniques such as Bayesian inference. Determining the elastic constants of built AM specimens with unknown polycrystalline texture is problematic, as any of the 21 possible independent unknown elastic constants can be non-zero. However, determining each of the 21 elastic constants by RUS inversion is not computationally feasible. Instead, texture coefficients (ODF) are transformed into second-order Hashin-Shtrikman bounds of the elastic constants. The calculated elastic constants are used to perform RUS inversions with a parallelizable sequential Monte Carlo (SMC) Python package. This is a novel use of RUS inversion to quantify the elastic constants and texture coefficients of arbitrarily anisotropic AM polycrystals.