Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers

Carter, Jennifer L. W.; Verma, Amit K.; Senanayake, Nishan M.

doi:10.1557/adv.2020.132

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Therefore, the algorithm is not able to accurately capture cuboidal particle shapes when they grow and evolve. The development and implementation of non-machine learning algorithms is a challenging task for those in the non-computer science disciplines because they require extensive knowledge of mathematics and computer science [17]. In addition, most existing mathematical algorithms are not compatible with graphics processing units (GPU).…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Approaches for Segmentation of Nanoscale Precipitates in Nickel-Based Superalloy IN718

Senanayake

Carter

2020

Integr Mater Manuf Innov

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Extracting accurate volume fraction and size measurements of γ″ and γ′ precipitates in iron-based superalloys from micrographs is challenging and conventionally involves manual image processing due to their smaller size, and similar crystal structures and chemistries. The co-precipitation of composite particles further complicates automated segmentation. In this work, different types of traditional machine learning approaches and a convolutional neural network (CNN) were compared to a non-machine learning approach, for the segmentation of the composite particles of γ″ and γ′ precipitates. The objective was to optimize metrics of segmentation accuracy and the required computational resources. The data set contains 47 experimentally generated scanning electron micrographs of IN718 alloy samples, computationally increased to 188 images (900 × 900 px). All algorithms are containerized using singularity, publicly available, and can be modified without dependencies. The CNN and the random forest models achieve 95% and 94% accuracy, respectively, on the test images with better computational efficiency than the non-machine learning algorithm. The CNN tested accurately over a range of imaging conditions.

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Section: Introductionmentioning

confidence: 99%

Computer Vision Approaches for Segmentation of Nanoscale Precipitates in Nickel-Based Superalloy IN718

Senanayake

Carter

2020

Integr Mater Manuf Innov

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“…4 Additionally, there are an array of unique challenges that arise in advancing the informaticsenabled design of structural materials because of the need for high-throughput quantitative evaluation of performance metrics across multiple time and length scales that require destructive measures. 5 This special topic aims to present some of the needs and limitations of the informatics toolsets for the design of structural materials. We have invited authors to speak on three challenges specifics to high-throughput metric evaluations necessary for informatics-enabled structural materials design: incorporation of cluster expansion theory with first-principles calculations; the value of novel and industrial-scale additive manufacturing for high-throughput synthesis, and characterization of mechanical properties; and the need to quantify microstructural distributions and extremes from three-dimensional datasets.…”

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confidence: 99%