Additive manufacturing (AM) maintains a wide process window that enables complex designs otherwise unattainable via conventional production technologies. However, the lack of confidence in qualifying AM parts that leverage AM process–structure–property–performance (PSPP) relationships stymies design optimization and adoption of AM. While continuing efforts to map fundamental PSPP relationships that cover the potential design space, we first need pragmatic and then long-term solutions that overcome challenges associated with qualifying AM-designed parts. Two pragmatic solutions include: (1) AM material specifications to substantiate process reproducibility, and (2) component risk categorization to associate system risk relative to part performance and required part quality. A novel qualification paradigm under development involves efficient prediction of part performance over wide-ranging PSPP relationships through targeted testing and computational simulation. This paper describes projects at Sandia National Laboratories on PSPP relationship discovery, these pragmatic approaches, and the novel qualification approach.
Traditional electronics assemblies are typically packaged using physically or chemically blown potted foams to reduce the effects of shock and vibration. These potting materials have several drawbacks including manufacturing reliability, lack of internal preload control, and poor serviceability. A modular foam encapsulation approach combined with additively manufactured (AM) silicone lattice compression structures can address these issues for packaged electronics. These preloaded silicone lattice structures, known as foam replacement structures (FRSs), are an integral part of the encapsulation approach and must be properly characterized to model the assembly stresses and dynamics. In this study, dynamic test data is used to validate finite element models of an electronics assembly with modular encapsulation and a direct ink write (DIW) AM silicone FRS. A variety of DIW compression architectures are characterized, and their nominal stress-strain behavior is represented with hyperfoam constitutive model parameterizations. Modeling is conducted with Sierra finite element software, specifically with a handoff from assembly preloading and uniaxial compression in Sierra/Solid Mechanics to linear modal and vibration analysis in Sierra/Structural Dynamics. This work demonstrates the application of this advanced modeling workflow, and results show good agreement with test data for both static and dynamic quantities of interest, including preload, modal, and vibration response.
This SAND report is an information packet for the Sandia Mechanics Challenge 2023, a benchmarking exercise for an international community of volunteer researchers to predict the behavior of an unfamiliar geometry and test scenario. The SAND report represents the experimental data described in the report. Once deemed UUR, this report and the associated data will be uploaded to the Materials Data Facility website and receive a DOI.
Here this paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a family of plasticity models where the yield surface is implicitly represented by a scalar-valued function. In particular, our focus is to introduce a practical framework that can be deployed for engineering analysis that employs a user-defined material subroutine (UMAT/VUMAT) for ABAQUS, which is written in FORTRAN. To accomplish this task while leveraging the back-propagation learning algorithm to speed up the neural-network training, we introduce an interface code where the weights and biases of the trained neural networks obtained via the PyTorch library can be automatically converted into a generic FORTRAN code that can be a part of the UMAT/VUMAT algorithm. To enable third-party validation, we purposely make all the data sets, source code used to train the neural-network-based constitutive models, and the trained models available in a public repository. Furthermore, the practicality of the workflow is then further tested on a dataset for anisotropic yield function to showcase the extensibility of the proposed framework. A number of representative numerical experiments are used to examine the accuracy, robustness and reproducibility of the results generated by the neural network models.
The mechanical behavior of partial-penetration laser welds exhibits significant variability in engineering quantities such as strength and apparent ductility. Understanding the root cause of this variability is important when using such welds in engineering designs. In Part II of this work, we develop finite element simulations with geometry derived from micro-computed tomography (μCT) scans of partial-penetration 304L stainless steel laser welds that were analyzed in Part I. We use these models to study the effects of the welds’ small-scale geometry, including porosity and weld depth variability, on the structural performance metrics of weld ductility and strength under quasi-static tensile loading. We show that this small-scale geometry is the primary cause of the observed variability for these mechanical response quantities. Additionally, we explore the sensitivity of model results to the conversion of the μCT data to discretized model geometry using different segmentation algorithms, and to the effect of small-scale geometry simplifications for pore shape and weld root texture. The modeling approach outlined and results of this work may be applicable to other material systems with small-scale geometric features and defects, such as additively manufactured materials.
The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics-informed deep-learning framework for the discovery of hyperelastic constitutive model parameterizations given full-field surface displacement data and global force-displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non-linear elastic behaviour, elastomer behaviour with finite strain lock-up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full-field experimental data are utilized to calibrate constitutive models under finite deformations.