Automated high-throughput tensile testing reveals stochastic process parameter sensitivity

Heckman, Nathan M.; Thomas, Ivanoff,; Roach, Ashley M.; Jared, Bradley Howell; Tung, Daniel Joseph; Brown-Shaklee, Harlan James; Todd, Huber,; Saiz, David J.; Koepke, Josh R.; Rodelas, Jeffrey; Madison, Jonathan D; Bradley, Salzbrenner,; Swiler, Laura Painton; Jones, Reese E.; Boyce, Brad Lee

doi:10.1016/j.msea.2019.138632

Cited by 41 publications

(8 citation statements)

References 30 publications

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“…The microstructural field φ(X) characterizes location-dependent inhomogeneity that influences the state of the material, where X is the position vector in the reference configuration of the sample. Examples of φ include phase in multiphase composites (Bouquerel et al, 2006), elastic modulus in a material with inclusions (Roduit et al, 2009) or pores (Heckman et al, 2020), and the local defect density in a defective material ( Čekada et al, 2007). Rotational equivariance requires…”

Section: Physical Problemmentioning

confidence: 99%

Mesh-Based Graph Convolutional Neural Networks for Modeling Materials With Microstructure

Frankel

Safta

Alleman

et al. 2022

J Mach Learn Model Comput

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Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering. Compared to feature-based and pixel-based convolutional neural network models, the proposed method has a number of advantages: (a) it is deep in that it does not require featurization but can benefit from it, (b) it has a simple implementation with standard convolutional filters and layers, (c) it works natively on unstructured and structured grid data without interpolation (unlike pixel-based convolutional neural networks), and (d) it preserves rotational invariance like other graph-based convolutional neural networks. We demonstrate the performance of the proposed network and compare it to traditional pixel-based convolution neural network models and feature-based graph convolutional neural networks on multiple large datasets.

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Section: Physical Problemmentioning

confidence: 99%

Mesh-Based Graph Convolutional Neural Networks for Modeling Materials With Microstructure

Frankel

Safta

Alleman

et al. 2022

J Mach Learn Model Comput

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“…To address this shortcoming, a high-throughput tensile method was developed for additively manufactured metals. The initial development involved manual loading of each tensile specimen into a system that employed drop-in grips, non-contact strain measurement, and software automation [21]; while a subsequent version also employed automated sample loading and dimensional measurements [22]. The high-throughput methods allowed for 10-100 times improvement in sampling rate compared to conventional test methods, with essentially no loss in data fidelity.…”

Section: Rare Weakest-link Failures In Additively Manufactured Stainl...mentioning

confidence: 99%

Microstructural Black Swans

Boyce

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Black swans are a metaphor for rare events with extreme consequences. In the domain of structural materials, black swans represent features in the microstructure that lead to catastrophic failure; as a result of their rarity, they are difficult to observe and often overlooked. These unusual weakest-link features are described variously as incipient, emergent, or anomalous. They give rise to localization, percolation, or avalanche events such as fracture, ductile rupture, dielectric breakdown, corrosion pit nucleation, and fatigue-crack initiation; as such, they are limiting cases in the concept of a representative volume. In this perspective, three examples are given of rare microstructural features and how they limit the mechanical reliability of structural metals. After taking stock of these examples, a future outlook considers the need for high-throughput testing and non-destructive characterization as well as detection algorithms and materials modelling strategies, including accelerated machine learning methods, that can capture anomalous events.

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“…In our previous work, we applied the high-throughput technique to characterize 1000 tensile tests under nominally identical conditions, 29 assessed the effect of the specimen size on the effective material properties, 14 and employed the results to identify optimal process parameters. 30 All those prior studies were performed on 316L, an alloy with extensive ductility and damage tolerance. Limited work has been done on size dependence of AM AlSi10Mg.…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Statistical Interrogation of Mechanical Properties with Build Plate Location and Powder Reuse in AlSi10Mg

Carroll

Exil

DeJong

et al. 2021

JOM

Self Cite

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Additive manufacturing (AM) allows agile, rapid manufacturing of geometrically complex components that would otherwise be impossible through traditional manufacturing methods. With this maturing manufacturing technology comes the need to adopt testing methods that are commensurate with the speed of additive manufacturing and take advantage of its geometric flexibility. High-throughput tensile testing (HTT) is a technique that allows a large number of tensile bars to be tested in a short amount of time. In the present study, HTT is used to evaluate AM AlSi10Mg produced using powder bed fusion with a Renishaw AM250 machine. Three parameters were varied in this study: (1) powder reuse history, (2) location on the build plate, and (3) size of the tensile specimen. For all parameter combinations, at least 22 specimens were tested; in several cases, over 40 were tested. This large dataset, consisting of over 500 tensile tests, permits Weibull statistical analysis and provides sufficient fidelity to isolate subtle trends that would have likely been missed in smaller, traditional datasets. The observed trends are rationalized in terms of the role of porosity and surface crust on mechanical response.

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Automated high-throughput tensile testing reveals stochastic process parameter sensitivity

Cited by 41 publications

References 30 publications

Mesh-Based Graph Convolutional Neural Networks for Modeling Materials With Microstructure

Mesh-Based Graph Convolutional Neural Networks for Modeling Materials With Microstructure

Microstructural Black Swans

High-Throughput Statistical Interrogation of Mechanical Properties with Build Plate Location and Powder Reuse in AlSi10Mg

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