3D printing of thermoplastics through local melting and deposition via Material Extrusion Additive Manufacturing provides a simple route to the near net-shape manufacture of complex objects. However, the mechanical properties resulting from these 3D printed structures tend to be inferior when compared to traditionally manufactured thermoplastics. These unfavorable characteristics are generally attributed to the structure of the interface between printed roads. Here, we illustrate how the molecular mass distribution for a model thermoplastic, poly(methyl methacrylate) (PMMA), can be tuned to enhance the Young’s modulus of 3D printed plastics. Engineering the molecular mass distribution alters the entanglement density, which controls the strength of the PMMA in the solid state and the chain diffusion in the melt. Increasing the low molecular mass tail increases Young’s modulus and ultimate tensile strength of the printed parts. These changes in mechanical properties are comparable to more complex routes previously reported involving new chemistry or nanoparticles. Controlling the molecular mass distribution provides a simple route to improve the performance in 3D printing of thermoplastics that can be as effective as more complex approaches.
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Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels
Abstract Graphene aerogels (GAs), a special class of 3D graphene assemblies, are well known for their exceptional combination of high strength, lightweightness, and high porosity. However, due to microstructural randomness, the mechanical properties of GAs are also highly stochastic, an issue that has been observed but insufficiently addressed. In this work, we develop Gaussian process metamodels to not only predict important mechanical properties of GAs but also quantify their uncertainties. Using the molecular dynamics simulation technique, GAs are assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to deduce mechanical properties. Results show that given the same density, mechanical properties such as the Young’s modulus and the ultimate tensile strength can vary substantially. Treating density, Young’s modulus, and ultimate tensile strength as functions of the inclusion size, and using the simulated GA results as training data, we build Gaussian process metamodels that can efficiently predict the properties of unseen GAs. In addition, statistically valid confidence intervals centered around the predictions are established. This metamodel approach is particularly beneficial when the data acquisition requires expensive experiments or computation, which is the case for GA simulations. The present research quantifies the uncertain mechanical properties of GAs, which may shed light on the statistical analysis of novel nanomaterials of a broad variety.
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- Award ID(s):
- 2119276
- NSF-PAR ID:
- 10353094
- Date Published:
- Journal Name:
- Nano Futures
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2399-1984
- Page Range / eLocation ID:
- 045004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.
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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
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Abstract In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) process is used to predict the space- and time-dependent temperature field during the multi-layer build process for Inconel 718 walls. The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds. The relationship between predicted cooling rate, microstructural features, and mechanical properties is examined, and cooling rate alone is found to be insufficient in giving quantitative property predictions. Because machine learning offers an efficient way to identify important features from series data, we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history. Very good predictions of material properties, especially ultimate tensile strength, are obtained using simulated thermal history data. To further interpret the convolutional neural network predictions, we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases. A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.more » « less
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Abstract Utilization of self‐healing chemistry to develop synthetic polymer materials that can heal themselves with restored mechanical performance and functionality is of great interest. Self‐healable polymer elastomers with tunable mechanical properties are especially attractive for a variety of applications. Herein, a series of urea functionalized poly(dimethyl siloxane)‐based elastomers (U‐PDMS‐Es) are reported with extremely high stretchability, self‐healing mechanical properties, and recoverable gas‐separation performance. Tailoring the molecular weights of poly(dimethyl siloxane) or weight ratio of elastic cross‐linker offers tunable mechanical properties of the obtained U‐PDMS‐Es, such as ultimate elongation (from 984% to 5600%), Young's modulus, ultimate tensile strength, toughness, and elastic recovery. The U‐PDMS‐Es can serve as excellent acoustic and vibration damping materials over a broad range of temperature (over 100 °C). The strain‐dependent elastic recovery behavior of U‐PDMS‐Es is also studied. After mechanical damage, the U‐PDMS‐Es can be healed in 120 min at ambient temperature or in 20 min at 40 °C with completely restored mechanical performance. The U‐PDMS‐Es are also demonstrated to exhibit recoverable gas‐separation functionality with retained permeability/selectivity after being damaged.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/2399-1984/ac3c8f
