Statistical characterization of microcellular polyurethane foams microstructure based on 2D and 3D image analysis

被引:5
|
作者
Le Saux, Matthieu [1 ,5 ]
Le Bail, Jean-Baptiste [1 ,2 ]
Becker, Justin [1 ,2 ]
Caer, Celia [1 ]
Charrier, Pierre [2 ]
Le Saux, Vincent [1 ]
Maheo, Laurent [3 ,4 ]
Marco, Yann [1 ]
机构
[1] ENSTA Bretagne, Brest, France
[2] Vibracoust, ESM Dept, Carquefou, France
[3] Univ Bretagne Sud, Bretagne, France
[4] Ecoles St Cyr Coetquidan, Guer, France
[5] ENSTA Bretagne, UMR CNRS 6027, IRDL, F-29200 Brest, France
关键词
Microcellular polyurethane elastomeric foam; cellular microstructure; scanning electron microscopy; x-ray micro-computed tomography; image processing; statistical analysis; SIZE DISTRIBUTIONS; PARTICLE-SIZE; ALGORITHMS; SHAPE;
D O I
10.1177/0021955X231215773
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
This paper presents protocols developed to quantitatively characterize the cellular microstructure of microcellular polyurethane foams, from scanning electron microscopy (2D) and X-ray micro-computed tomography (2D and 3D) data. The objectives are to provide, for both techniques: (i) a detailed description of the analysis steps based on open source Python algorithms; (ii) a method for automatic, robust and objective detection of the cells to limit user's biases; (iii) a statistical description of fraction, size, shape and spatial distribution of cells. The study considers 12 samples with densities ranging from about 400 to 600 kg m-3 and pore sizes from a few micrometers to several hundred micrometers. In addition, the database obtained is used to investigate the reliability of 2D measurements to describe the cellular microstructure statistics.
引用
收藏
页码:395 / 417
页数:23
相关论文
共 50 条
  • [1] Comparison of 2D and 3D dynamic image analysis for characterization of natural sands
    Li, Linzhu
    Iskander, Magued
    ENGINEERING GEOLOGY, 2021, 290
  • [2] 2D/3D image analysis as a tool for tissue engineering
    Martin, I
    Toso, C
    Beltrame, F
    Diaspro, A
    Fato, M
    Facchini, A
    Marcacci, M
    DePasquale, V
    Strocchi, R
    Zaffagnini, S
    MINERVA BIOTECNOLOGICA, 1997, 9 (01) : 11 - 16
  • [3] 3D Pose Estimation Based on Reinforce Learning for 2D Image-Based 3D Model Retrieval
    Nie, Wei-Zhi
    Jia, Wen-Wu
    Li, Wen-Hui
    Liu, An-An
    Zhao, Si-Cheng
    IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 23 (23) : 1021 - 1034
  • [4] Comparison of 2D and 3D image-based aggregate morphological indices
    Kutay, M. Emin
    Ozturk, Hande I.
    Abbas, Ala R.
    Hu, Chichun
    INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2011, 12 (04) : 421 - 431
  • [5] 2D/3D Multimode Medical Image Alignment Based on Spatial Histograms
    Ban, Yuxi
    Wang, Yang
    Liu, Shan
    Yang, Bo
    Liu, Mingzhe
    Yin, Lirong
    Zheng, Wenfeng
    APPLIED SCIENCES-BASEL, 2022, 12 (16):
  • [6] A theoretical 2D image model for locating 3D targets
    Mao, Jiafa
    Xiao, Gang
    Sheng, Weiguo
    Hayat, Tasawar
    Liu, Xiaohui
    INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS, 2017, 94 (07) : 1430 - 1450
  • [7] Review on 2D and 3D MRI Image Segmentation Techniques
    Shirly, S.
    Ramesh, K.
    CURRENT MEDICAL IMAGING REVIEWS, 2019, 15 (02) : 150 - 160
  • [8] Measuring linearity of curves in 2D and 3D
    Rosin, Paul L.
    Pantovic, Jovanka
    Zunic, Jovisa
    PATTERN RECOGNITION, 2016, 49 : 65 - 78
  • [9] CLN: Cross-Domain Learning Network for 2D Image-Based 3D Shape Retrieval
    Nie, Weizhi
    Zhao, Yue
    Nie, Jie
    Liu, An-An
    Zhao, Sicheng
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 32 (03) : 992 - 1005
  • [10] Converting 2D Image into Sequence of Curves on 3D Flat Model
    Suciati, Nanik
    Harada, Koichi
    PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTER TECHNOLOGY AND DEVELOPMENT, VOL 1, 2009, : 397 - 401