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2024 | Buch

Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4

Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics

herausgegeben von: Sharlotte L.B. Kramer, Emily Retzlaff, Piyush Thakre, Johan Hoefnagels, Marco Rossi, Attilio Lattanzi, François Hemez, Mostafa Mirshekari, Austin Downey

Verlag: Springer Nature Switzerland

Buchreihe : Conference Proceedings of the Society for Experimental Mechanics Series

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Über dieses Buch

Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 of the Proceedings of the 2023 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the fourth volume of five from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on a wide range of topics and includes papers in the following general technical research areas:

AM Composites and Polymers

Dynamic Behavior of Additively Manufactured Materials and Structures

Joint Residual Stress and Additive Manufacturing

ML for Material Model Identification

Novel AM Structures

Novel Processing and Testing of Additively Manufactured Materials

Plasticity and Complex Material Behavior

Virtual Fields Method

Inhaltsverzeichnis

Frontmatter
Quantifying Residual Stresses Generated by Laser-Powder Bed Fusion of Metallic Samples
Abstract
We use numerical modeling to predict residual stresses and deformations of thin metallic structures manufactured by laser-powder bed fusion. The effect of L-PBF process on residual deformations of thin quasi-2D structures is expected to be more substantial and complex than for thicker/bulk or axisymmetric components. Two types of geometries are considered: a thin horizontal plate for residual force measurements and thin vertical plates for residual deformations and support removal experiments. In both cases knowledge of the initially deformed shape and internal residual stresses will affect experimental interpretation. The numerical scheme used (ANSYS Additive Suite) involves weakly coupled thermomechanical simulations in a commercially available finite element package. It is shown that the simulations are in qualitative and general quantitative agreement with the experimental measurements within this numerical framework. Additionally, it is shown that the provided numerical framework can be used to predict the effect of support removal sequence on the final geometry of thin metallic structures.
Pouria Khanbolouki, Rodrigo Magana-Carranza, Eann Patterson, Chris Sutcliffe, John Lambros
Loading-Unloading Compressive Response and Energy Dissipation of Liquid Crystal Elastomers and Their 3D Printed Lattice Structures at Various Strain Rates
Abstract
Nematic liquid crystal elastomers (LCEs) are a unique class of network polymers with potential for excellent mechanical energy absorption and dissipation capacity due to their ability to change the nematic director under mechanical loading (sometimes called soft-elasticity) in addition to the viscoelastic behavior of the remaining polymer network. This additional inelastic mechanism makes them appealing as candidate damping materials in a variety of applications from vibration to impact. The lattice structures made from the LCEs provide further mechanical energy absorption and dissipation capacity associated with packing out the porosity.
Understanding the extent of mechanical energy absorption versus dissipation depends on the mechanical stress-strain response under both loading and unloading. In the past, the loading-unloading stress-strain response was only obtained within quasi-static (slow) strain rates on standard material test frames. In this study, we used a newly developed bench-top linear actuator to characterize the loading-unloading compressive response of polydomain and monodomain LCE polymers and polydomain LCE lattice structures with two different porosities (nominally, 62% and 85%) at both low and intermediate strain rates at room temperature. As a reference material, a bisphenol A (BPA) polymer with a similar glass transition temperature (9 °C) as the nematic LCE (4 °C) was also characterized at the same conditions for comparing to the LCE polymers. Based on the loading-unloading stress-strain curves, the energy absorption and dissipation for each material at different strain rates (0.001, 0.1, 1, 10 and 90 s−1) were able to be calculated. The strain-rate effect on the mechanical response and energy absorption and dissipation behaviors was determined.
Bo Song, Dylan Landry, Thomas Martinez, Christopher Chung, Kevin Long, Kai Yu, Chris Yakacki
Residual Stress Induced in Thin Plates During Additive Manufacturing
Abstract
Additive manufacturing is a technique for producing complex geometry engineering parts relatively quickly and cheaply; however, residual stresses induced in the part during manufacture can result in significant distortion of the build. In this study, nickel-chromium alloy (Inconel 625) geometrically-reinforced thin plates have been additively manufactured using laser-powder bed fusion, that have comparable flatness to those built subtractively. The residual stresses induced in the thin plates from manufacture are deduced by measuring out-of-plane displacements using stereoscopic digital image correlation. The results demonstrate that residual stresses cause potentially severe out-of-plane displacements which can be alleviated by using buttress supports to reinforce the plate edges during the build. In both landscape and portrait orientation builds, out-of-plane displacement increased upon release from the baseplate but was reduced by incremental release.
Eann A. Patterson, John Lambros, Rodrigo Magana-Carranza, Christopher J. Sutcliffe
Investigating the Effects of Acetone Vapor Treatment and Post Drying Conditions on Tensile and Fatigue Behavior of 3D Printed ABS Components
Abstract
Fused Deposition Modeling (FDM), an additive manufacturing/3D printing process, is widely used where the material is melted, extruded, and deposited in layers to build up the desired object. The applications of FDM technologies have significantly increased recently not only for rapid prototyping but also for mass production of finished products. In 3D printing, parts are usually built in discrete layers. Hence, this manufacturing process results in a certain amount of structural uncertainty in the form of discontinuities, voids, and poor inter-layer bonding. In our previous research, we successfully investigated the differences in the ultimate strength and fatigue life for 3D printed Acrylonitrile Butadiene Styrene (ABS) components built by various build/layer orientations. Our previous research successfully highlighted the ultimate strengths and fatigue life, including SN Curves. However, there is a need for further research to improve the tensile strength and fatigue life of the 3D printed ABS components. This research explores effects of the surface treatment on the tensile strength and fatigue life of the 3D printed ABS components with various layup-orientation. In this study, Acetone Vaper Smoothing (AVS) method was used as the surface treatment of the 3D printed ABS components. Our research found that the AVS method could reduce stress concentrations on the surface and structural uncertainty of the 3D printed ABS components to improve the tensile and fatigue strength. However, these results were occurred after adjusting the Acetone vapor exposure and improving the drying methods because Acetone weakened the layer bonding of the ABS and reduced the tensile strength and fatigue life of the 3D printed ABS components. This research provides the optimal conditions of the Acetone Vapor exposure time and the drying time.
Heechang Bae, Nicholas Blair, Matthew Michaelis, Awlad Hossain
Mechanics of Novel Double-Rounded-V Hierarchical Auxetic Structure: Finite Element Analysis and Experiments Using Three-Dimensional Digital Image Correlation
Abstract
Auxetic design is increasingly being used in applications that demand high strength-to-weight ratio. Auxetic structures are also used for crash safety and cushioning applications. Hierarchical auxetic structures inspired by natural cellular structures show a negative Poisson ratio. This phenomenon is caused by inherent geometric constraints in these structures. Due to the advancements in additive manufacturing, high-performance computing, and shape optimization algorithms, developing high-performance auxetic structures has emerged as an active area of research. Recently, a sinusoidal design of repeating cell with smooth and curved members, referred to as double-U hierarchical (DUH) structure, has been proved to perform better than the traditional double-V hierarchical (DVH) structure. However, the possibility of improving the existing DVH design has not been explored in the literature. In the present work, we fill this research gap by conducting a systematic study wherein the extent of curviness and roundness in its unit cell is varied and evaluated using finite element analysis and experiments. We call our improved version of DVH as DRVH (double-rounded-V hierarchical) auxetic structure. Finite element analyses of DUH and DRVH structures are conducted by simulating quasi-static loading (0.16667 mm/s) and impact loading (1,00,000 mm/s) conditions. The total simulation time is 30 s for quasi-static loading, whereas it is 0.00005 s for impact loading when a 5-mm displacement is applied at one end of the DUH and DRVH auxetic structures. The comparative analyses are carried out by evaluating internal energy, plastic deformation, kinetic energy, total energy, displacements, contact forces, reaction forces, and stress values. History and field outputs are requested at every 0.5 s time increment for quasi-static loading, whereas they are requested at every 10−6 s for impact loading. Experiments are conducted on DRVH structures manufactured through fused deposition modeling (FDM), which is the most commonly used additive manufacturing technology. The filament used in this work is PLA Flax filament supplied by Nanovia®. It is a biodegradable material, which is comprised of poly lactic acid (PLA) and Flax fiber. Three-dimensional digital image correlation is performed to validate the deformation of DRVH structure. The combination of straightness and roundness of the DRVH members not only provides more room for absorbing impact energy than the DUH design but also reduces peak stress and enhances auxetic behavior and static collapse stress.
Rajesh Kumar, Iniyan Thiruselvam
Repeatability of Residual Stress in Replicate Additively Manufactured 316L Stainless Steel Samples
Abstract
Additive manufacturing presents an opportunity to produce complex component geometries with exceptional strength properties in 316L stainless steel. The high temperature gradients during manufacturing produce near-yield tensile residual stresses often near exterior component surfaces, which are indicative of the process history. In this study the repeatability of the residual stress is evaluated across five identically prepared, replicate samples sequentially built on a single 3D Systems ProX CMP200 machine with fixed process parameters. The samples are nominally cube shaped with edge lengths of 50.8 mm. Near surface residual stresses are measured using both strain gage hole-drilling and X-ray diffraction. Within the first 1 mm of the top surface, all measurements show equi-biaxial tensile residual stresses with peak magnitude near 500 MPa (close to yield strength). One build is found to be an outlier, with residual stress different than in the four other builds. Data from hole-drilling and X-ray based techniques are in general agreement. Among the four similar builds, average normal residual stress in the near-surface (0–0.25 mm depth) is 424–427 MPa and standard deviation of residual stress among the four builds is 25–39 MPa. Average residual stress in the mid-depth (0.25–0.90 mm depth) is 442–453 MPa and standard deviation is 8.8–8.9 MPa. While the high tensile near surface residual stress may be of concern (because of potential increased risk for subcritical cracking by fatigue or corrosion), variation of residual stress is small and similar to levels seen in other metallic forms such as plate or die forgings.
Christopher R. D’Elia, Daniel R. Moser, Kyle L. Johnson, Michael R. Hill
Acoustic Nondestructive Characterization of Metal Pantographs for Material and Defect Identification
Abstract
Mechanical metamaterials offer an approach to design materials beyond bulk properties by changing their geometry. Due to the complex architecture, the fabrication of these material systems is often enabled by additive manufacturing. However, production-related defects can occur and affect the material functionality, so that rapid and reliable characterization methods are required. In this work, we studied acoustic analysis, specifically a combination of classical resonance analysis and decay time observation, for nondestructive characterization of metamaterials. We focused on metallic pantographs which are metamaterials with high elongation. All samples were manufactured by laser powder bed fusion with three different base materials: NiTi, Fe-based, and Ti6Al4V. Experiments complimented by simulations were carried out to evaluate vibration behavior stimulated by a low energy impact. Geometrical design parameters and major defects such as missing elements resulted in changes in the acoustic spectrum as well as the decay time of the signal. Simulations enable the interpretation of the occurring modes and offer a possibility to study a much wider parameter space. Acoustic characterization of metamaterials is promising for scaling the technology of metamaterials for industrial applications due to the simplicity and low costs. In the future, further investigations are needed to optimize the simulations for complex geometries and evaluate alternatives for the acoustic stimulation of the samples.
Silviya M. Boyadzhieva, Lea S. Kollmannsperger, Florian Gutmann, Thomas Straub, Sarah C. L. Fischer
Rapid Prototyping of a Micro-Scale Spectroscopic System by Two-Photon Direct Laser Writing
Abstract
The development of two-photon 3D lithographic technologies has enabled the fabrication of 3D optics at sub-micron resolution. The freedom allotted by this technology allows for the fabrication of miniaturized complex optical components, such as freeform micro-optics and diffractive optical elements. Additionally, the ability to rapidly prototype these micro-optical components facilitates the development of custom-tailored optical systems for specialized applications. The goal of this work is the construction of a prototype micro-optical system that enables quantitative absorbance spectroscopy for use in the visible and near infrared (VIS/NIR) regions. Employing a combination of optical design software and two-photon direct laser writing (TP-DLW) we seek to demonstrate functionalization of an optical fiber with a miniaturized spectrophotometer, integrable with mobile devices. The methods used to produce this miniaturized optical system facilitates functionalization of chip-on-tip technology, as a low-cost approach for realization of integrated spectrophotometers in applications from quality assurance in the food and drug industries, to spectral tissue sensing in medicine. Presented in this work are the representative results of the performance and integrability of a prototyped micro-spectrophotometer probe at the tip of an optical fiber.
Anthony Salerni, Cosme Furlong
Bioinspired Interfaces for Improved Interlaminar Shear Strength in 3D Printed Multi-material Polymer Composites
Abstract
Additively manufactured soft-hard composite materials are significant because they combine the benefits of both soft and hard materials, creating a new class of materials with unique properties. The interfaces between the soft and hard phases are the most susceptible to failure and play a crucial role in fully utilizing the potential of each component in the composite material. This research introduces a new approach to improve the shear strength of interfaces in soft-hard polymer composites made using additive technology. The method involves designing and constructing a suture interface between the soft and hard polymers using the Fused Filament Fabrication (FFF) process. The overlap distance, a crucial parameter in the FFF method, is utilized to modify the size of the soft protrusions at the suture interface, resulting in improved interlaminar shear strength. The interface morphology is analyzed through microscopic evaluations of samples cut from the fabricated specimens, both in terms of quality and quantity. The interlaminar shear strength is measured using the short beam test method, and the failure process is further examined through digital image correlation measurements. The findings suggest that the interlaminar shear strength and interface stiffness gradually increases as the protrusion length grows. These results could serve as a useful reference for developing multi-material polymer composites with stronger and more resilient interfaces through additive manufacturing.
Umut Altuntas, Demirkan Coker, Denizhan Yavas
Thermo-mechanical Characterization of High-Strength Steel Through Inverse Methods
Abstract
The thermo-mechanical characterization of the plastic behavior of high-strength steel is extremely important for the correct simulation of the forming process in the industry. From an experimental point of view, several tests at different temperatures are necessary to suitably identify the constitutive behavior of the material. In this work, a different approach was used and a mixed experiment was designed where the specimen undergoes different temperatures in different zones in order to test a relatively large range of temperatures with a single experiment. This can be done using a Gleeble system that allows to produce a parabolic temperature gradient along a specimen thanks to resistance heating and cooled jaw carriers. The specimen shape was optimized to avoid large strain localization in the zone of maximum temperature using multiphysics simulations able to reproduce the heating system and the deformation process. From this experiment, the constitutive parameters of a thermo-mechanical model can be extracted using an inverse method, e.g. the Virtual Fields Method (VFM) or the Finite Element Model Updating (FEMU). In particular, VFM can be used if both the strain field and the temperature field are known from the experiments, for instance using digital image correlation and an IR-camera, while FEMU can be used in other cases.
Marco Rossi, Luca Morichelli, Steven Cooreman
A Multi-testing Approach for the Full Calibration of 3D Anisotropic Plasticity Models via Inverse Methods
Abstract
Sheet metals are characterized by an anisotropic behavior which plays a crucial role in the prediction of their plastic and failure. The accurate description of their mechanical response generally requires the definition of advanced material models that often demand material information from several orientations and stress states. Recently, inverse methods such as the virtual fields method (VFM) or the finite element model updating (FEMU), coupled with a full-field measurement technique, have been distinguished as efficient strategies for the calibration of complex material model. The use of heterogeneous strain fields, in fact, offers a larger amount of material information compared to the classical standard test, enriching the identification process and, in general, reducing the experimental effort for the calibration.
In this study, a new inverse identification framework is proposed for the calibration of a full-scale anisotropic plasticity model. The idea is to include the data from different tests to overcome the main limitation of 3D plasticity models with the inclusion of constitutive information about the shear anisotropy along the thickness direction.
The inverse identification procedure employs full-field information from two main experiments: a biaxial tensile test on a cruciform specimen for the calibration of the coefficients expressing the planar anisotropy, and an innovative Iosipescu-like test for the through-thickness shear ones. Virtual experiments based on finite element simulations are used to evaluate the error associated to the identification procedure, with the aim to optimize of the whole characterization process.
Attilio Lattanzi, Mattia Utzeri, Marco Rossi, Dario Amodio
Finite Element Based Material Property Identification Utilizing Full-Field Deformation Measurements
Abstract
A direct approach based on finite element formulation is described to determine material property distribution in a nominally heterogeneous material subject to tensile/compression loading. The formulation is developed for plane stress applications using basic theoretical constructs, resulting in a computational framework that has a matrix form [A] {E} = {F}, where the [A] matrix components are known functions of measured strain components and nodal coordinates, {F} components are known functions of body forces, applied loads and reactions and {E} components are the unknown material properties at discrete locations. A methodology for material property identification is outlined, involving measured strain components at discrete locations amid varying levels of random noise. The presented results illustrate the accuracy of the approach as well as it’s sensitivity to noise.
Sreehari Rajan Kattil, Subramani Sockalingam, Michael A. Sutton, Tusit Weerasooriya
Data-Driven Material Models for Engineering Materials Subjected to Arbitrary Loading Paths: Influence of the Dimension of the Dataset
Abstract
Engineering materials are subjected to complex stress states, mutable environmental conditions, and strain rates during their operating life. It is therefore paramount to develop methodologies capable of capturing their behaviour from experimental data, in order to predict their response under different thermo-mechanical sequences and histories. This is particularly relevant for materials that exhibit different strength in tension, compression, shear, and their combination, such as titanium alloys, magnesium alloys, composites, etc. The adoption of machine learning data-driven models obtained from arbitrary thermo-mechanical loading experiments provides an accurate and computationally efficient way to predict the response of engineering materials during loading sequences typical of real case scenarios. This study presents how neural networks with different structures can capture the response of materials measured during experiments carried out under arbitrary sequences of load. The effect of the data set size on the accuracy of the surrogate model is also assessed.
Burcu Tasdemir, Vito Tagarielli, Antonio Pellegrino
Data-Driven Methodology to Extract Stress Fields in Materials Subjected to Dynamic Loading
Abstract
Full-field stress determination is critical for dynamic loading condition when the stress fields are non homogeneous. Recent advances in high-speed experimental mechanics have led to methods that estimate stress from full-field deformation measurements. However, these methods require multiple numerical differentiation of displacement data, making them less accurate due to noise content in experimental measurements. In order to efficiently tackle noisy displacement data and predict accurate stress fields, a methodology based on neural networks is developed. Specifically, physics-informed neural networks are employed so that the information embedded in physical laws is also utilized along with the experimental measurements. The proposed method to inversely estimate the stress is illustrated by applying it to the impact of rigid mass on an elastic rod that generates a sharp stress discontinuity. A multi-network model is developed where independent feedforward neural networks approximate the displacement and stress. Physical laws are incorporated through equilibrium equations that effectively guide the method toward the right solution. It is shown that the method provides reliable estimates of stress even if the stress field is discontinuous and noise present in the data.
Vijendra Gupta, Addis Kidane
Metadaten
Titel
Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4
herausgegeben von
Sharlotte L.B. Kramer
Emily Retzlaff
Piyush Thakre
Johan Hoefnagels
Marco Rossi
Attilio Lattanzi
François Hemez
Mostafa Mirshekari
Austin Downey
Copyright-Jahr
2024
Electronic ISBN
978-3-031-50474-7
Print ISBN
978-3-031-50473-0
DOI
https://doi.org/10.1007/978-3-031-50474-7

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