2023 International Solid Freeform Fabrication Symposium

Permanent URI for this collectionhttps://hdl.handle.net/2152/123551

The 34th Annual International Solid Freeform Fabrication (SFF) Symposium – An Additive Manufacturing Conference, was held from August 14-16, 2023. There were 589 registrants from 9 countries. The total number of scheduled oral and poster presentations was 513. The meeting consisted of a Monday morning plenary, 56 parallel technical sessions and a poster session. The plenary session included an excellent mixture of presentations from Industry, US National Laboratories, and Universities. The Plenary Session also included presentations from the recipients of the 2023 International Outstanding Young Researcher in Freeform and Additive Manufacturing Award Dr. Niocholas Meisel, from Penn State University and Anthony Rollett from Carnegie Mellon University who won the International Freeform and Additive Manufacturing Excellence (FAME) Award.

There are over XX papers in this conference proceedings. Papers marked “REVIEWED” in the title area were peer reviewed by two external reviewers. The Table of Contents file and Author-Attendee file have links to all the papers. We have sequentially numbered the pages of the papers to facilitate citation. Manuscripts for this and all preceding SFF Symposia are available for free download at the conference website: https://www.sffsymposium.org; select the “Proceedings Archive” pull-down menu item.

The editors would like to thank the Organizing Committee, the session chairs, the attendees for their enthusiastic participation, and the speakers both for their significant contribution to the meeting and for the relatively prompt delivery of the manuscripts comprising this volume. We are grateful to TMS conference management staff for their significant contributions to the meeting planning and proceedings production, particularly Jeff Gnacinski, Tess De Jong, Kellye Parsson, Colleen Madore, and Trudi Dunlap. We look forward to the continued close cooperation of the additive manufacturing community in organizing the Symposium. We also want to thank the National Science Foundation (CMMI- 2226705) for supporting this meeting financially by providing 75 student registration fee waivers. Funding from the Office of Naval Research is also gratefully acknowledged. The meeting was organized within the Walker Department of Mechanical Engineering and the Center for Additive Manufacturing and Design Innovation (CAMDI) at The University of Texas at Austin.

Joe Beaman took over as the Chair of the Organizing Committee The next SFF Symposium is August 12-14, 2024 at the Hilton Austin Hotel in Austin, Texas USA. The conference website will become active in mid-January 2024.

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Recent Submissions

Now showing 1 - 20 of 171
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    Development of a Testbench for Additive Manufacturing Data Integration, Management, and Analytics
    (University of Texas at Austin, 2023) Yang, Chen-Wei; Kuan, Alexander; Li, Sheng-Yen; Lu, Yan; Kim, Jaehyuk; Cheng, Fan-Tien; Yang, Haw-Ching
    The NIST Additive Manufacturing (AM) Data Integration Testbench is a platform designed to evaluate data models, communication methods, and data analytics for AM industrialization. This paper describes a reference framework for AM data integration, named AMIF, and the design of the testbench based on AM Integration Framework (AMIF) for testing the integration of in-process data acquisition, real-time feature extraction, process control, and predictive models under a data management system. A specification of this testbench is developed to manage and stream voluminous data captured by high-speed cameras and performing data analytics using common information models and functional interfaces. The integration of the data, models, and computer tools sends operational decisions to an AM machine in real time. On top of the real-time control functions, AM data integration with MES and ERP systems is also included using a high-performance data warehouse for long-term data archiving and metadata management. The architecture of this testbench is illustrated in this work. AMIF can guide AM practitioners and system integrators to build their integrated AM manufacturing systems for production. The NIST AM testbench’s plug-and-play features allow both internal and external researchers and developers to assess the effectiveness of their individual data models, data analytics, and decision-making algorithms on the systems engineering level.
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    Development of 3D Printable Part Library for Easy to Manufacture Components for Educational and Competitive Robotics
    (University of Texas at Austin, 2023) Dwivedi, Indira; Dwivedi, Rajeev; Dwivedi, Bharat; Rebbapragada, Arun; Rebbapragada, Arka
    Educational and competitive robotics enable hands on learning and experimentation. Despite cost effective and ease of access of open source micro-controllers, drives and sensors, the structural components and brackets continue to be very expensive. Motivated by the Robotics for Everyone initiative, we are developing many easy-to-manufacture parts that will allow learners to easily 3D print parts for (1) Structural assembly of robot chassis (2) Sensor mounting (3) Electronic control mounting (4) Power supply (5) Various power drives. The ecosystem of the robotic components is developed around extrusion structures and tubular elements and 3D printing is used for building the parts for testing and qualifying. Fixtures for mounting cameras for advanced machine learning and computer vision experiments are provided.
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    DESIGN FOR(E!) ADDITIVE MANUFACTURING: IN SEARCH OF A COMPREHENSIVE DESIGN CHALLENGE SUITABLE ACROSS AM EDUCATION
    (University of Texas at Austin, 2023) Meisel, Nicholas
    Modern engineering design education relies heavily on the concept of problem-based learning (PBL). Driven by the constructivist theory of education, PBL enables students to build knowledge organically, rather than through rote memorization. As such, design for additive manufacturing (DfAM) education also tends to emphasize the use of PBL to encourage student learning. Unfortunately, dedicated DfAM education is still nascent. The result is a wide range of educators leveraging an equally wide, and often unproven, range of design challenges to support DfAM PBL. Because of this, there is the possibility that a chosen design challenge will not represent AM as a true end-use manufacturing process nor promote a design space that can benefit from the full consideration of all opportunistic and restrictive DfAM concepts. In this paper, the author discusses the creation and implementation of a comprehensive design challenge that is suitable across the range of AM education. Specifically, the author proposes the use of a golf putter DfAM design challenge. This concept draws from lessons learned over years of DfAM instruction at undergraduate and graduate levels and is based in the need for three key aspects for a successful DfAM challenge in education: (1) clarity, (2) applicability, and (3) demonstrability.
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    Streaming in Additive Manufacturing: Analyzing the Impact on the Powder Bed Fusion of Metals Process Chain
    (University of Texas at Austin, 2023) Kolter, M.; Schleifenbaum, J.H.
    Streaming is a popular concept in the music and movie industry and has helped solve problems related to file distribution, storage capacity and intellectual property protection. In recent years, streaming has also become a research topic for the manufacturing sector, e.g., to collect data for predictive maintenance, advanced machine control concepts, or over-the-air updates. After initial studies have investigated the feasibility of streaming for additive manufacturing technologies, the question how streaming will affect the process chain remains open. In the music and movie industries, new business models and customer experiences have been created, leading to billion-dollar businesses and the creation of companies such as Netflix and Spotify. By drawing parallels from music and media to the AM industry this paper gives an overview of potential innovations fostered by streaming in the domain of Additive Manufacturing (AM) such as file distribution and novel process control possibilities. Furthermore, the impact of these innovations on the process chain is discussed by the example of Powder Bed Fusion of Metals with Laser Beam (PBF-LB/M).
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    DEVELOPMENT OF HIGHLY FILLED BIO-BASED COMPOSITES FOR SUSTAINABLE, LOW-COST FEEDSTOCK: PROCESSING EFFECTS ON POROSITY AND FIBER ALIGNMENT
    (University of Texas at Austin, 2023) Copenhaver, Katie; Lamm, Meghan; Hubbard, Amber
    A poly(lactic acid) composite with a high loading of bio-based fibers was developed using a combination of high-aspect ratio (AR) wood pulp and low-AR wood flour along with viscosity modifiers to maximize mechanical performance, maintain processability, and lower the cost and embodied energy of the resulting feedstock. An optimized composite formulation containing 40 wt.% of a blend of high- and low-AR natural fibers with a rice bran-based wax processing aid was scaled up to produce pellet feedstock using twin screw extrusion, and materials were compression and injection molded to investigate the effect of fiber alignment on material performance. The feedstock was then printed on the Big Area Additive Manufacturing system at Oak Ridge National Laboratory. Print parameters including temperature gradients, screw and gantry speeds, layer times, and nozzle designs were varied to minimize sharkskinning, warpage, and porosity of the final parts. A strong effect of the nozzle size on the resulting porosity was observed, and consistent trends between decreasing porosity, increasing fiber alignment, and increasing mechanical performance were identified after printing with different nozzles, compression molding, and injection molding.
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    TOWARDS THE LIMITS IN COPPER LATTICE PRODUCTION VIA FIBER LASER POWDER BED FUSION
    (University of Texas at Austin, 2023) Smet, M.; Li, G.; Goossens, L.R.; Buls, S.; Van Hooreweder, B.
    Additive manufacturing of copper, by means of Laser Powder Bed Fusion (LPBF), paves the way for innovation in thermal systems and heat transfer devices. Recent simulations have shown that by interchanging typical fin designs with more complex structures, an overall improvement in pressure drop and weight can be obtained while offering the same thermal performance. Here, small-scale lattice structures are especially of interest for AM as they form a reliable, periodic infill. However, until now, their study has been mainly theoretical. To analyze these structures in more detail, an in-house built LPBF machine at KU Leuven has been successfully used to manufacture pure copper parts. Measurements showed a conductivity exceeding 100%IACS, which is the result of low contamination and low porosity in the as-built material. In this work, the parameter optimization for thin-walled lattices is discussed, the limitations in terms of minimal feature size are described and physical mechanisms behind these limitations are uncovered.
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    Towards experimental process parameter development for Ti-6Al-4V TPMS lattice structures with application to small scale dental implants using micrographs
    (University of Texas at Austin, 2023) Jahn, A.; Li, H.; Emminghaus, N.; Melnyk, T.; Hermsdorf, J.; Kaierle, S.
    Ti-6Al-4V is a widely used alloy in implant engineering and lattice structures can be applied to locally match the stiffness of the implant to the stiffness of bone. Triply periodic minimal surface (TPMS) structures are popular due to their curved surface, which supports a good manufacturability and osseointegration of the implant. Tests with different TPMS structures showed a strong interaction between design factors and manufacturing parameters resulting in the need for individual parameter development. However, to the best of our knowledge, the most work in the current literature focusses on mechanical and biological examinations of TPMS structures manufactured with standard parameters. As process parameters influence the structural properties, the optimum values for further analysis may not have been investigated (e.g., their influence on microstructure and mechanical properties). In this work, a design of experiments approach is used to develop process parameters. As computer tomography scans are resource intensive for large scale parameter development, a sparser approach using micrographs for porosity analysis is introduced. Small structures with unit cell size as small as 1.0 mm are fabricated on a laser powder bed fusion industrial machine. Our initial studies show that the design factor pore size is negligible in comparison to wall thickness when optimizing internal porosity.
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    Thermal Stress Prediction in Laser Powder Bed Fusion
    (2023) Liu, Tao; Kinzel, Edward C.; Leu, Ming C.
    This research applies Green's function solutions to simulate temperature and thermal stress fields in laser powder-bed fusion (LPBF) processes. Using a semi-infinite domain and 2D Gaussian laser profiles, the analytical model achieves high computational efficiency, has the potential for realtime controls and predictions in LPBF processes. The model highlights the role of principal stresses in determining crack formations, aligning closely with experimental results.
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    Strength Enhancement of cellular structures through selective reinforcement of elements based on analytical modeling
    (University of Texas at Austin, 2023) Koju, Naresh; Yang, Li
    This work investigates the strength enhancement of 2D cellular structures via individual element thickness optimization based on the analytical model for critical elements. To focus on the investigation of the enhancement method, a rather simplified perfectly elastic material property was assumed, and an analytical model was utilized to identify the critical element of several cellular structure designs. Stepwise element thickness enhancement was utilized to investigate the effectiveness of overall strength enhancement. The results indicate that the strength of cellular structures can be improved by selectively reinforcing critical elements. In addition, the enhanced cellular structures also exhibit altered fracture failure characteristics that could potentially be exploited for more application objectives.
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    RESIDUAL STRESS AND DEFORMATION ANALYSIS OF INCONEL 718 ACROSS VARYING OVERHANGS IN LASER POWDER BLOWN DIRECTED ENERGY DEPOSITION
    (University of Texas at Austin, 2023) Hernandez, A.J.; Garcia, D.; Watanabe, K.I.; Gradl, P.R.; Wheeler, K.; Hafiychuk, Halyna; Wicker, R.B.; Medina, F.
    Any metal that is subjected to rapid heat and cooling will undergo the development of residual stresses. As they experience intense temperature fluctuations, this will consequently alter the way the material will behave. This issue proves to be of great concern within additive manufacturing. That said, the presence of temperature fluctuations is more prominent in Directed energy deposition (DED), whereas other methods of manufacturing are more prominent in the pre- or post- printing process. This in turn means the deformation, as well as the redistribution of the residual stresses within pieces, are subject to variance by several process parameters set during a print. By using the Inconel 718 alloy feedstock in RPMI’s Laser Powder Directed Energy Deposition (LP-DED) printer, a series of coupons with four different overhang angles and laser power outputs will determine how these changes thermo-mechanically affect the prints through the use of FEA simulations and scans.
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    OUT-OF-PLANE MECHANICAL PROPERTIES OF ADDITIVELY MANUFACTURED FRACTAL REINFORCED STRUCTURES
    (University of Texas at Austin, 2023) Martínez-Magallanes, Mario; Cuan-Urquizo, Enrique; Ramirez-Cedillo, Erick; Roman-Flores, Armando
    Architected materials are an emergent kind of materials that gain their physical properties from their rationally designed micro-structures. They are normally conformed by regular unit-cells repetition, but other variations, such as hierarchal, aperiodic, and graded arrangements have also been explored as well. Here we propose an approach consisting of using fractal geometry to control the mechanical response of the metamaterials. We designed a set of 11 different arrangements based on the self-filling Hilbert fractal, the set consisted of 3 different iteration orders at 3 different matching relative densities, and two other graded arrangements. The samples were fabricated using a Micro-LCD 3D-printer and tested under out-of-plane loads. The test was performed using a texturometer with a spherical probe impregnated with red paint to characterize the conformability of the samples. Force and displacement were recorded to compare the mechanical response of the samples against the fractal parameters and obtain the structure-property relation.
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    Mechanical Surface Treatment of Polymer Parts Produced by FFF
    (University of Texas at Austin, 2023) Dietrich, S.; Karcher, B.; Popp, U.; Scholz, J.
    The surface structure in the form of waviness and roughness as well as near surface density of FFF parts represents a major issue with respect to mechanical performance especially under fatigue loading. Mechanical surface treatments like shot peening or rolling are commonly used techniques, especially for metal components, to reduce surface roughness, increase surface densification and create beneficial residual stress states in the surface layer. In this study, a rolling process has been applied intermittently with the layer-wise FFF process and the effect on the surface state has been investigated using laser scanning and optical microscopy as well as microcomputed tomography. A process window with different rolling tools and rolling paths has been identified and analysed. The results show clearly advantageous properties regarding an improved surface roughness, with a higher densification gradient in the first perimeter tracks of the FFF extrusion strategy as well as sharper corners being realized.
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    MATERIALS SCREENING METHODOLOGY FOR ADDITIVE MANUFACTURING IN BIOREACTOR TECHNOLOGY
    (University of Texas at Austin, 2023) Schorzmann, J.; Gerstl, H.; Tan, Z.; Sprenger, L.; Lu, H.-H.; Taumann, S.; Wimmer, M.; Boccaccini, A.R.; Salehi-Muller, S.; Dopper, F.
    Biofabrication is used to fabricate complex tissues/organs inspired by their native structures using additive manufacturing (AM) techniques and bio-inks (biopolymers enriched with living cells). Electroactive cells such as skeletal muscle function via electrical signals and therefore, their optimum in vitro functionality requires electrical conductivity and electrical stimulations. AM can be used to precisely fabricate a bioreactor for a dynamic culture of cells and bioengineered tissues and electrical stimulation of them. In this study, we focused on a material selection methodology for AM of bioreactors with selective electrical conductivity based on Reuter [1]. The important material requirements for bioreactors are biocompatibility, chemical stability, electrical conductivity, and the capability of being sterilized. However, there is no standardized procedure for selecting materials, that are appropriate for AM of bioreactors. Our study comprises three phases which deductively narrowed down the material selection; these phases are the determination of material requirements, pre-selection, and fine selection of suitable materials. With the proposed method, a material selection for AM of functional bioreactors (consisting of bioreactor housing and integrated additively manufactured electrodes for electrical stimulation of the cells) could be efficiently made. For the bioreactor housing, two of the investigated materials, high-temperature polylactic acid (HTPLA) and polypropylene (PP) meet all requirements. The materials of the bioreactor electrodes could be narrowed down to polyethylene with copper particles (PE-Cu) and poly lactic acid with graphene nanoplates (PLA-GNP), where PE-Cu fulfilled all requirements besides the biocompatibility. PLA-GNP matches all requirements besides the high temperature resistance. For a final selection of the material for the bioreactor electrodes, further tests are required. However, this approach enabled to reduce the amount of biocompatibility testing from 16 different materials to only four (- 75%), saving material, time, capacity and costs.
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    Material Jetting of Suspension System Components.
    (University of Texas at Austin, 2023) Lipton, Jeffrey I.
    Material Jetting has demonstrated great promise in being able to produce complex functionalities using multi-material printing. Despite this potential material jetting has struggled to find applications in direct part production. Here we show how material jetting can be used to produce viscoelastic energy absorbers for large displacement applications in harsh environments. We generate printed components to act as the core of a suspension system on a recumbent trike. The 3D printed dampers allowed for improvements of the ride experienced. Through long term exposure studies, we demonstrate that techniques and methods previously applied to the absorption of vibration in indoor power tool applications can be extended to outdoor environments.
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    Machine Learning Assisted Mechanical Metamaterial Design for Additive Manufacturing
    (University of Texas at Austin, 2023) Wang, Jier; Panesar, Ajit
    Metamaterials, widely studied for its counterintuitive property such as negative Poisson’s ratio, negative refraction, negative thermal expansion, and employed in various fields, are recognised to provide foundation for superior multiscale structural designs. However, current mechanical metamaterial design methods usually relay on performing sizing optimisations on predefined topology or implementing time-consuming inverse homogenisation methods. Machine Learning (ML), as a powerful self-learning tool, is considered to have the potential of discovering metamaterial topology and extending its property bounds. This work considers the use of Neural Networks (NNs), (De-Convolutional Neural Networks) DCNNs and Generative Adversarial Networks (GANs) to speed up the generation of new topologies for metamaterials. NNs and DCNNs are trained to inversely generate metamaterial designs based on the input target effective macroscale properties, whilst the generator in GANs is expected to output diverse metamaterial microstructures with random noise inputs. This work highlights the potential of data-driven approaches in Design for Additive Manufacturing (DfAM) as an alternative to the time-consuming, conventional methods.
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    IN-SITU DEFECT DETECTION FOR LASER POWDER BED FUSION WITH ACTIVE LASER THERMOGRAPHY
    (University of Texas at Austin, 2023) Breese, P.P.; Becker, T.; Oster, S.; Metz, C.; Altenburg, S.J.
    Defects are still common in metal components built with Additive Manufacturing (AM). Process monitoring methods for laser powder bed fusion (PBF-LB/M) are used in industry, but relationships between monitoring data and defect formation are not fully understood yet. Additionally, defects and deformations may develop with a time delay to the laser energy input. Thus, currently, the component quality is only determinable after the finished process. Here, active laser thermography, a nondestructive testing method, is adapted to PBF-LB/M, using the defocused process laser as heat source. The testing can be performed layer by layer throughout the manufacturing process. We study our proposed testing method along experiments carried out on a custom research PBF-LB/M machine using infrared (IR) cameras. Our work enables a shift from post-process testing of components towards in-situ testing during the AM process. The actual component quality is evaluated in the process chamber and defects can be detected between layers.
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    Improving the Mechanical Response of the IWP Exo-skeletal Lattice Through Shape Optimization
    (University of Texas at Austin, 2023) Fisher, Joseph W.; Miller, Simon W.; Bartolai, Joseph; Simpson, Timothy W.
    Triply Periodic Minimal Surfaces have been identified as good candidates for the generation of lattice structures produced with additive manufacturing. These TPMS-based lattice structures avoid sharp features that are characteristic of strut-based lattice structures because of their constant zero mean curvature. Although studies have explored part-scale optimization using TPMS-based lattice structures, they have only varied the volume fraction by changing the level set in the approximate surface equations. By defining new parameterizations in the approximate surface equation, we can redistribute volume within the lattice structure at any volume fraction. In this paper, we introduce an approach for optimization of this new parameterization of TPMS equations using the Borg multi-objective evolutionary algorithm. We demonstrate this framework on the IWP exo-skeletal lattice under uniaxial compression. A relationship between the new parameters and the level set is derived for designs on the Pareto frontier of the optimized IWP TPxS. The performance of the Pareto optimal designs and the efficacy of the optimization approach are shown by comparing to the standard IWP lattice and four other lattices that share the same topology. The optimized designs are implemented and shared in custom nTopology blocks.
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    Hybrid Geometry/Property Autoencoders for Multi-Lattice Transitions
    (University of Texas at Austin, 2023) Baldwin, Martha; Meisel, Nicholas A.; McComb, Christopher
    Additive manufacturing has revolutionized structural optimization by enhancing component strength and reducing material requirements. One approach used to achieve these improvements is the application of multi-lattice structures. The performance of these structures heavily relies on the detailed design of mesostructural elements. Many current approaches use data-driven design to generate multi-lattice transition regions, making use of models that jointly address the geometry and properties of the mesostructures. However, it remains unclear whether the integration of mechanical properties into the data set for generating multi-lattice interpolations is beneficial beyond geometry alone. To address this issue, this work implements and evaluates a hybrid geometry/property machine learning model for generating multi-lattice transition regions. We compare the results of this hybrid model to results obtained using a geometry-only model. Our research determined that incorporating physical properties decreased the number of variables to address in the latent space, and therefore improves the ability of generative models for developing transition regions of multi-lattice structures.
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    GUIDED MANUAL DESIGN FOR ADDITIVE MANUFACTURING OF TOPOLOGICALLY OPTIMIZED LEGACY TOOLING PARTS
    (University of Texas at Austin, 2023) Luben, Hannah; Meisel, Nicholas
    Design for Additive Manufacturing (DfAM) is a unique conceptual way to adapt a part for Additive Manufacturing (AM). While some of the choices made in DfAM become second nature to seasoned AM designers, inexperienced designers may not know the nuances involved in what is still a developing manufacturing technology. Topology Optimization (TO) in particular tends to create organic shapes that may not be immediately conducive to printing through AM. This paper proposes a comprehensive workflow tool to guide a designer, regardless of their experience, through the decision-making process inherent to DfAM. The guide helps the designer manually edit a legacy tooling design into a topologically optimized part that is readily manufacturable through AM. Discussion of a relevant case study follows the outline of the design tool to exemplify its use.
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    Fatigue Endurance Investigation of Post-processed Surfaces of LPBF Ti-6Al-4V under Flexural Stress
    (University of Texas at Austin, 2023) Banuelos, C.; Ramirez, B.; De la Cruz, A.; Nabil, S.T.; Arrieta, E.G.; Wicker, R.B.; Medina, F.
    Numerous research works can be found focusing on fatigue properties of AM components, however most of this literature is focused on uniaxial testing. Because the very few actual components under uniaxial loading conditions found in any application, it is also important to investigate fatigue performance under loads that produce combined stresses, such as bending. This project investigates the fatigue endurance of LPBF Ti-6Al-4V specimens subjected to four different surface finishing prost-processes (milled, ground, polished and abrasive media). The test consisted of a force-controlled cyclic load applied on the specimen in a 4-point bending setup until fracture. The study incorporated mechanical and optical techniques to measure and quantify the characteristic surface roughness of the post-processes. Additionally, failure mechanisms are discussed on fractographs. The data analyses suggested that internal defects commonly present in additively manufactured parts had a more significant impact on the fatigue life than surface roughness of post-processed parts.