Additive Manufacturing Benchmarks 2022 (AM-Bench 2022): Mechanical Behavior I
Program Organizers: Brandon Lane, National Institute of Standards and Technology; Lyle Levine, National Institute of Standards and Technology
Monday 1:30 PM
August 15, 2022
Room: Regency Ballroom I & II
Location: Hyatt Regency Bethesda
Session Chair: Orion Kafka, National Institute Of Standards And Technology
1:30 PM Invited
Mechanical Property Modeling of AM Alloys and its Application in Accelerated Qualification and Certification: Abhinav Saboo1; Qiaofu Zhang1; Tanner Kirk2; Jiadong Gong3; Gregory Olson4; 1QuesTek Innovations LLC; 2Texas A&M University; 3Questek Innovations LLC; 4Massachusetts Institute of Technology
Qualification and certification require extensive mechanical property data generation which can become costly and time intensive, especially when ideal processing and heat treatment conditions for the alloys are not already known. A model-based qualification procedure can accelerate this procedure by optimizing the processing and heat treatment conditions, thereby reducing testing, while also providing increased confidence in determination of minimum properties. These concepts are embodied in QuesTek’s Accelerated Insertion of Materials (AIM) methodology, which has been successfully utilized in qualification and certification of high-performance steels. In this talk, state-of-the-art models for accurate predictions of static mechanical properties like yield strength and ultimate tensile strength for additively manufactured components will be discussed. Its application on different alloys – AlSi10Mg and IN625 – will be demonstrated and its integration with QuesTek’s AIM methodology will also be discussed.
2:00 PM
Use of Extreme Value Analysis to Determine Data Requirements for Defect Characterization and Predict Variation in Fatigue Performance: Tharun Reddy1; Mahya Shahabi2; David Scannapieco3; Austin Ngo3; Anthony Rollett1; John Lewandowski3; Sneha Prabha Narra1; 1Carnegie Mellon University; 2Worcester Polytechnic Institute; 3Case Western Reserve University
A major factor in the fatigue life of fracture-critical parts is the effect of process-induced defects and the critical pore/defect size. Prediction of critical pore/defect size in different process regimes of a laser powder bed fusion processed part could provide invaluable information for the widening application of additive manufacturing. This study uses extreme value analysis to predict critical pore/defect size in Ti-6Al-4V bend bar samples using the 2D cross-sectional porosity data. The results confirm that the pore/defect density and the required model precision determine the data required to characterize part porosity, the maximum pore/defect size prediction from process conditions used for one sample applies to another sample with similar porosity distribution, and the peaks-over-threshold model yields the best predictions. An analysis framework is used to demonstrate applicability for predicting critical pore size in fatigue samples and compare to initiating defect size and defect density on the fracture surface.
2:20 PM
Process-Structure-Properties Simulations for Predicting Fatigue Indicator Parameters of Additive Manufactured Ti-6Al-4V with Quantified Uncertainty: Joshua Pribe1; Saikumar Yeratapally1; Brodan Richter2; Patrick Leser2; George Weber2; Edward Glaessgen2; 1National Institute of Aerospace; 2NASA Langley Research Center
Metals produced by additive manufacturing (AM) have complex, spatially heterogeneous microstructures. Microstructural details depend on the build process and can lead to significant variability in the mechanical properties. Understanding and quantifying uncertainty in process-structure-property relationships is thereby an important step in qualifying metal AM parts. This work presents process-structure-properties simulations of AM Ti-6Al-4V with the goal of relating fatigue indicator parameters to process variability. Dimensions of the melt pool and surrounding heat-affected zone during an AM build are predicted using an analytical temperature solution. The results are combined with a Monte Carlo Potts model to predict two- and three-dimensional microstructures. Mechanical loading of the microstructures is simulated using an elasto-viscoplastic fast Fourier transform formulation that determines the micromechanical stress and strain fields in each grain. This in turn enables prediction of microstructure-sensitive fatigue indicator parameters. Distributions of the fatigue indicator parameters and their dependence on selected process parameters are presented.
2:40 PM
Decoupling the Effect of Geometry and Texture on the Mechanical Response of Additively Manufactured IN625 Thin-walled Elements: Arunima Banerjee1; Mo-Rigen He1; Jeff Rossin2; William Musinski3; Paul Shade3; Marie Cox3; Tresa Pollock2; Kevin Hemker1; 1Johns Hopkins University; 2UCSB; 3Air Force Research Laboratory
Additively manufactured (AM) metallic components have highly anisotropic microstructures spanning multiple length scales. The relationship between microstructure and mechanical properties of printed structures is complicated by the introduction of additional design parameters, such as build orientation and geometric variations. This study was undertaken to decouple the contribution of texture and geometry on the mechanical response of thin-walled IN625 T-elements fabricated by laser powder bed fusion. The T-elements were printed at either 40° or 90° inclinations from the build plate resulting in a 10% difference in the ligament widths. T-elements underwent a standard stress-relief heat treatment and subsets of each build orientation were subsequently homogenized at 1150°C for 90 minutes. Milli-scale tests indicate that the 90° T-elements are stiffer in both heat-treated states. However, a higher fraction of <111>- and <110>-oriented grains along the loading direction in the stress-relieved 40° T-elements suggests a stiffer response than the 90° counterpart.
3:00 PM Break