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Meeting MS&T21: Materials Science & Technology
Symposium Accelerating Materials Science with Big Data and Machine Learning
Presentation Title Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
Author(s) Kavindu Wijesinghe, Janith Wanni, Natasha Banerjee, Sean Banerjee, Ajit Achuthan
On-Site Speaker (Planned) Kavindu Wijesinghe
Abstract Scope Advanced experimental capabilities that enable detailed characterization of microscopic deformation of coupon specimens under simple loading conditions for studying the influence of specific microstructural features on mechanical properties is a growing need in the field of materials science. Building such capabilities require powerful data analysis methods to extract complex characteristics of microscopic deformation hidden in the raw image data. In this presentation, we report the development and demonstration of a data analysis framework using deep learning methods. The framework consists of a trained Mask R-CNN model combined with a regional instance segmentation algorithm for feature detection, an intersection over union based multi-object tracking algorithm to track segmented features as they deform, and kinematics models to extract the material characteristics of the deforming instances. For validation, we characterized the microscopic deformation of an additively manufactured 316L stainless steel coupon specimen under quasi-static tensile testing.
Proceedings Inclusion? Undecided


A Data-driven Simulator for High-throughput Prediction of Electromigration-mediated Damage in Polycrystalline Interconnects
Accelerating Discovery in Computational Materials Science Using CAMD
Bridging the Gap between Literature Data Extraction and Domain Specific Materials Informatics
Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
Considerations for Interpretability, Reliability, And Data-efficiency in Machine Learning Properties of Solid-state Materials
Data Science as Bridge – Materials Characterization and Modeling
Deep Learning-enabled Prediction of Mechanical Properties of Metallic Microlattice Structures Using Uniaxial Compression Videos
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Predicting Glass Behaviour from Optical Microscopy Images Using Interpretable Machine Learning
Scalable Gaussian Processes for Predicting the Optical, Physical, Thermal, and Mechanical Properties of Inorganic Glasses Using Compositions for Large Datasets
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Slip Band Characterization with Microtensile Testing Using Digital Image Processing
There is No Time for Science as Usual
Topology Optimization for Two-phase Composites Using Active Learning Based Gaussian Process Regression

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