Additive Manufacturing Fatigue and Fracture IV: Toward Confident Use in Critical Applications: Property Prediction I
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; Steve Daniewicz, University of Alabama; Nima Shamsaei, Auburn University; John Lewandowski, Case Western Reserve University; Mohsen Seifi, ASTM International/Case Western Reserve University
Thursday 8:30 AM
February 27, 2020
Room: 10
Location: San Diego Convention Ctr
Session Chair: Nima Shamsaei, Auburn University
8:30 AM Invited
Fatigue and Fracture Analysis of Additive Manufactured Metals
for Critical Applications: Ali Fatemi1; 1University of Memphis
Additive manufacturing (AM) has recently gained much interest due to the many advantages it offers, as compared to the traditional subtractive manufacturing methods. Some distinguishing features of AM metals include surface roughness, defects, residual stresses, and anisotropy of the properties. Most components made of AM processes are subjected to cyclic loads, therefore, fatigue performance is an important consideration, particularly in safety critical applications. This presentation provides an overview of the aforementioned issues using recent data generated using AM Ti-6Al-4V and 17-4 PH stainless steel. Specimens were made by L-PBF and subjected to axial, torsion, and combined loadings. A variety of conditions including surface roughness, thermo-mechanical treatment, damage mechanisms and crack paths, cyclic deformation, crack nucleation and growth, and stress concentration effects are considered. In addition, application of the specimen data to component level fatigue performance is also considered by using an additively manufactured tension link as an illustrative example.
9:00 AM
Improvement of Fatigue Strength in Lightweight Selective Laser Melted Alloys By In-situ and Ex-situ Composition and Heat Treatment: Mustafa Awd1; Jan Johannsen2; TSZ Tung Chan3; Mohamed Merghany3; Claus Emmelmann2; Frank Walther1; Felix Stern3; 1Department of Materials Test Engineering (WPT), TU Dortmund University; 2Fraunhofer Research Institution for Additive Manufacturing Technologies (IAPT); 3TU Dortmund University
Selective laser melting is a powder-bed-fusion process that is applied to different alloys. Thus, it is essential to study what are the different process variables that affect the static, quasistatic, and cyclic mechanical properties. In this contribution, we introduce two examples of alloys: AlSi (Al-12Si, Al-10Si-Mg) and Ti-6Al-4V. The influence of controlled cooling and degassing mechanisms of residual gases is investigated by structural analysis in electron microscopy, and X-ray computed tomography. Controlled cooling through platform heating or multiexposure treatments increased the dendritic width in AlSi alloys and decomposed alpha prime in Ti-6Al-4V. The alteration was a cause for enhanced ductility and slowing of crack propagation. The cyclic deformation is tracked during mechanical testing and is simulated in FE software using a high-throughput methodology to calculate Woehler curves based on Fatemi-Socie damage parameters. The cyclic deformation simulation is in agreement with the experimental data and quantified cyclic damage using Fatemi-Socie parameters.
9:20 AM
Microscale Analysis of the Synergistic Effects of Notch and Post-processed Microstructures in AM Ti-6Al-4V: Lara Draelos1; Peeyush Nandwana2; Ankit Srivastava1; 1Texas A&M University; 2Oak Ridge National Laboratory
Electron beam powder bed fusion of Ti-6Al-4V results in anisotropic mechanical behavior. The microstructure of AM Ti-6Al-4V can be modified by post-process heat treatments to engineer the extent of anisotropy. However, post-processing results in microstructures that promote strain-localization. Specifically, sub-transus heat treatment results in strain-localization normal to the build/loading direction whereas super-transus heat treatment results in random strain-localization patterns at length-scales greater than microstructural length-scale. Our objective is to unravel the effect of post-process microstructures on the deformation and fracture response of AM Ti-6Al-4V under complex deformation fields such as those that develop in the presence of a notch. To this end, smooth and notched specimens of as-processed and post-processed materials are deformed under tension inside a microscope. This allows us to capture both the macroscopic and the microscopic mechanical response. We will present the results correlating the effects of notch, microstructure, microscale deformation and macroscale response of the material.
9:40 AM
A Zone-based, Probabilistic Damage Tolerance Framework for AM Components: James Sobotka1; R. Craig McClung1; Michael Enright1; Jonathan Moody1; Yi-Der Lee1; Vikram Bhamidipati1; 1Southwest Research Institute
Additive manufacturing (AM) processes can introduce a variety of potential material anomalies (e.g., lack-of-fusion defects, gas-entrapped pores, and surface roughness) throughout a component. Under cyclic loading, these anomalies can lead to crack formation, fatigue crack growth, and fracture. For higher-criticality parts, a zone-based probabilistic damage tolerance framework has been proposed by Gorelik (Int. J. Fatigue, 2017) to ensure structural integrity of AM parts. This framework enables analyses that combine material variability, geometric complexity, and defect distributions to determine zone-level and component-level probabilities of failure. This probabilistic framework enables analysts to quantify uncertainty inherent in materials/components susceptible to inherent material anomalies that can potentially form a crack anywhere. This presentation discusses this framework and its implementation within the engineering software DARWIN®. This presentation will showcase aspects of probabilistic damage tolerance analysis relevant to AM, including the incorporation of residual stresses, non-destructive inspection, and inverse analysis modes.
10:00 AM Break
10:20 AM Invited
A Fatigue Life Approach for Additively Manufactured Structures: Rainer Wagener1; Benjamin Möller1; Matilde Scurria1; Thilo Bein1; 1Fraunhofer Lbf
In the framework of a numerical fatigue approach the cyclic material properties have to be known. Therefore, stress- and strain-controlled fatigue tests have to be performed in order to derive the material properties in order to describe appropriate the cyclic structure behavior under service loading conditions. With respect to the typical component sizes and built times, small scale specimens are preferred, providing that the specimens behavior can be transferred to the components. Considering that, methods to transfer the specimens behavior to components are required. This transfer function have to manage effects related to the different sizes of specimens and component and stress-strain development caused by the loading conditions and history. At the end, a modified fatigue approach concept, which takes into account the main influences on the fatigue and fulfills the industrial requirements of optimized ratio of computation time and validity of the fatigue approach, will be derived.
10:50 AM
Additive Manufacturing of Fatigue Resistant Austenitic Stainless Steel: Jonathan Pegues1; Michael Roach2; Nima Shamsaei1; 1Auburn University; 2University of Mississippi Medical Center
Additive manufacturing promises to revolutionize the production of parts/assemblies across several industries. Many of these applications are structural and require a thorough characterization of the fatigue behavior. One of the biggest promises attributed to additive manufacturing of fatigue critical parts is the ability to tailor the microstructure to meet specific loading conditions and improve performance. Process induced defects and surface roughness inherent to additive manufacturing, however, have proven to be severely detrimental to the fatigue behavior of these materials. This work characterizes the effects of process conditions on the resulting structure of an additively manufactured austenitic stainless steel. The relationship between the material structure including microstructure, surface roughness, and defect distribution and the resulting mechanical performance is also investigated to establish the process-structure-property relationships. Results indicate that improved fatigue resistance for austenitic stainless steels can be achieved by avoiding the typical failure mechanisms associated with their wrought counterparts.
11:10 AM
Deriving the Structural Fatigue Behavior of Additive Manufactured Components: Rainer Wagener1; Matthias Hell1; Matilde Scurria1; Thilo Bein1; 1Fraunhofer Lbf
Additive manufacturing technologies of metals are known to be time consuming and they usually lead to inhomogeneous microstructures. In order to consider the property gradients within a numerical fatigue approach, appropriate material properties ,describing the local cyclic material behavior, are required. Small-scaled specimens are useful for deriving suitable properties, if test facilities with a high resolution and reproducibility are available. A combination of piezo ceramic driven test rig, which enables the performance of stress-controlled fatigue test with high frequencies, and an E-Cylinder test rig, designed for low frequency strain-controlled fatigue tests, are used for the derivation of the Fatigue Life Curve, a continuous Wöhler-curve from Low Cycle Fatigue up to the Very High Cycle Fatigue regime, and the cyclic stress-strain behavior of representative structure elements. Additionally using Incremental Step Tests, the cyclic structural behavior could be derived and successively implemented in the performance of a numerical fatigue life approach.
11:30 AM
Additive Manufacturing-enhanced Durability Prediction Supported by a Machine-learning Based Material Model: Nicolas Lammens1; Matthias Schulz1; Stefan Straesser1; Hunor Erdelyi1; 1Siemens Industry Software NV
Predictable fatigue performance of AM components is a significant challenge because of the large range of printing parameters selectable and the occurrence of artefacts such as key-hole and lack of fusion pores which cannot always be resolved. A solution is the use of durability software accounting for these typical artefacts. Essential in such a simulation, is the availability of a material model that can account for the impact of multiple AM artefacts. Such a material model is difficult to develop given the many complex interactions between parameters, and the cost of test campaigns.As part of the Flemish FATAM project, an AM-enhanced durability prediction software was developed supported by a machine-learning based material model. The flexibility and limited a-priori assumptions offered by the machine learning allows the material model to adapt as more test data becomes available and detect cross-sensitivities which would be hard to predict using conventional material modelling.