Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images

Lu, Mingjian; Venkat, Sameera Nalin; Augustino, Jube; Meshnick, David; Jimenez, Jayvic Cristian; Tripathi, Pawan K.; Nihar, Arafath; Orme, Christine A.; French, Roger H.; Bruckman, Laura S.; Wu, Yinghui

doi:10.1007/s40192-023-00320-8

Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images

Journal Article · Fri Dec 08 04:00:00 EST 2023 · Integrating Materials and Manufacturing Innovation

DOI:https://doi.org/10.1007/s40192-023-00320-8· OSTI ID:2281476

Lu, Mingjian ^[1]; Venkat, Sameera Nalin ^[1]; Augustino, Jube ^[1]; Meshnick, David ^[1]; Jimenez, Jayvic Cristian ^[1]; Tripathi, Pawan K. ^[1]; Nihar, Arafath ^[1]; Orme, Christine A. ^[2]; French, Roger H. ^[1]; Bruckman, Laura S. ^[1]; ^[1]

Case Western Reserve University, Cleveland, OH (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Phase transformations in materials systems can be tracked using atomic force microscopy (AFM), enabling the examination of surface properties and macroscale morphologies. In situ measurements investigating phase transformations generate large datasets of time-lapse image sequences. The interpretation of the resulting image sequences, guided by domain-knowledge, requires manual image processing using handcrafted masks. Here this approach is time-consuming and restricts the number of images that can be processed. Her in this study, we developed an automated image processing pipeline which integrates image detection and segmentation methods. We examine five time-series AFM videos of various fluoroelastomer phase transformations. The number of image sequences per video ranges from a hundred to a thousand image sequences. The resulting image processing pipeline aims to automatically classify and analyze images to enable batch processing. Using this pipeline, the growth of each individual fluoroelastomer crystallite can be tracked through time. We incorporated statistical analysis into the pipeline to investigate trends in phase transformations between different fluoroelastomer batches. Understanding these phase transformations is crucial, as it can provide valuable insights into manufacturing processes, improve product quality, and possibly lead to the development of more advanced fluoroelastomer formulations.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344; NA0004104

OSTI ID:: 2281476

Report Number(s):: LLNL--JRNL-853058; 1079244

Journal Information:: Integrating Materials and Manufacturing Innovation, Journal Name: Integrating Materials and Manufacturing Innovation Journal Issue: 4 Vol. 12; ISSN 2193-9764

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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