An official website of the United States government
Additive manufacturing has provided the ability to manufacture complex structures using a wide variety of materials and geometries. Structures such as triply periodic minimal surface (TPMS) lattices have been incorporated into products across many fields due to their unique combinations of mechanical, geometric, and physical properties. Yet, the near limitless possibility of combining geometry and material into these lattices leaves much to be discovered. This article provides a dataset of experimentally gathered tensile stress-strain curves and measured porosity values for 389 unique gyroid lattice structures manufactured using vat photopolymerization 3D printing. The lattice samples were printed from one of twenty different photopolymer materials available from either Formlabs, LOCTITE AM, or ETEC that range from strong and brittle to elastic and ductile and were printed on commercially available 3D printers, specifically the Formlabs Form2, Prusa SL1, and ETEC Envision One cDLM Mechanical. The stress-strain curves were recorded with an MTS Criterion C43.504 mechanical testing apparatus and following ASTM standards, and the void fraction or “porosity” of each lattice was measured using a calibrated scale. This data serves as a valuable resource for use in the development of novel printing materials and lattice geometries and provides insight into the influence of photopolymer material properties on the printability, geometric accuracy, and mechanical performance of 3D printed lattice structures. The data described in this article was used to train a machine learning model capable of predicting mechanical properties of 3D printed gyroid lattices based on the base mechanical properties of the printing material and porosity of the lattice in the research article [1].
more »
« less
Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning
Abstract Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles.
more »
« less
- Award ID(s):
- 2119643
- PAR ID:
- 10463631
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Additive manufacturing (AM) of metals often results in parts with unfavorable mechanical properties. Laser peening (LP) is a high strain rate mechanical surface treatment that hammers a workpiece and induces favorable mechanical properties. Peening strain hardens a surface and imparts compressive residual stresses improving the mechanical properties of a material. This work investigates the role of LP on layer-by-layer processing of 3D printed metals using finite element analysis. The objective is to understand temporal and spatial residual stress development after thermal and mechanical cancellation caused by cyclically coupling printing and peening. Results indicate layer peening frequency is a critical process parameter affecting residual stress redistribution and highly interdependent on the heat generated by the printing process. Optimum hybrid process conditions were found to exists that favorably enhance mechanical properties. With this study, hybrid-AM has ushered in the next evolutionary step in AM and has the potential to profoundly change the way high value metal goods are manufactured.more » « less
-
Mechanical stimuli such as strain, force, and pressure are pervasive within and beyond the human body. Mechanoresponsive hydrogels have been engineered to undergo changes in their physicochemical or mechanical properties in response to such stimuli. Relevant responses can include strain-stiffening, self-healing, strain-dependent stress relaxation, and shear rate-dependent viscosity. These features are a direct result of dynamic bonds or non- covalent/physical interactions within such hydrogels. The contributions of various types of bonds and intermolecular interactions to these behaviors are important to more fully understand the resulting materials and engineer their mechanoresponsive features. Here, strain-stiffening in carboxymethylcellulose hydrogels crosslinked with pendant dynamic-covalent boronate esters using tannic acid is studied and modulated as a function of polymer concentration, temperature, and effective crosslink density. Furthermore, these materials are found to exhibit self-healing and strain- memory, as well as strain-dependent stress relaxation and shear rate-dependent changes in gel viscosity. These features are attributed to the dynamic nature of the boronate ester crosslinks, inter-chain hydrogen bonding and bundling, or a combination of these two intermolecular interactions. This work provides insight into the interplay of such interactions in the context of mechanoresponsive behaviors, particularly informing the design of hydrogels with tunable strain- stiffening. The multi-responsive and tunable nature of this hydrogel system therefore presents a promising platform for a variety of applications.more » « less
-
Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. Using two or more constituent materials with different physical and mechanical properties, it becomes possible to construct interpenetrating phase composites (IPCs) with 3D interconnected structures to provide superior mechanical properties as compared to the conventional reinforced composites with discrete particles or fibers. The mechanical properties of IPCs, especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young’s modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy. Such superfast and accurate prediction of mechanical properties of IPCs could significantly accelerate the IPC structural design and related composite designs for desired mechanical properties.more » « less
-
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturingAbstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.more » « less
