Faculty of Technology and Metallurgy, University of Belgrade, Belgrade, Serbia.
Faculty of Technology, University of Banja Luka, Banja Luka, Bosnia and Herzegovina.
PLoS One. 2020 Nov 16;15(11):e0241665. doi: 10.1371/journal.pone.0241665. eCollection 2020.
The development of automated software and the device for determination of wicking of textile materials, using open-source ImageJ libraries for image processing, and newly designed additional algorithm for the determination of threshold, is presented in this paper. The description of the device, design of the open-source software "Kapilarko", as well as an explanation of the steps: image processing, threshold determination and reading of wicking height, are provided. We have also investigated the possibility of using the artificial neural networks for automatic recognition of the wicking height. The results showed that the recognition of the wet area of the sample, based on the application of artificial neural networks was in a very good agreement with the experimental data. The device's utility for the measurement of wicking ability of textile materials was proved by testing various knitted fabrics. The constructed device has the advantages of providing automated measurement and minimization of the subjective errors of the operators; extremely fast or long-term measurements; digital recording of results; consistency of experimental conditions; possibility of using water instead of colors and, last but not least, low cost of the device. Considering the importance and frequent measurements of wicking ability of textile materials, the advantages of the presented device, as well as the fact that commercial software without publishing the source-code, are used for most of the available devices, we believe that our idea to design the automated software and device by applying the "open-source" approach, will be of benefit to scientists and engineers in using or improving wicking experiments.
本文介绍了一种自动化软件和设备的开发，该软件和设备用于测定纺织材料的芯吸性能，使用了开源的 ImageJ 库进行图像处理，并为阈值确定设计了新的附加算法。本文描述了设备的设计、开源软件“Kapilarko”的设计，以及图像处理、阈值确定和芯吸高度读取步骤的说明。我们还研究了使用人工神经网络自动识别芯吸高度的可能性。结果表明，基于人工神经网络的应用，对样品湿区的识别与实验数据非常吻合。通过对各种针织面料的测试，证明了该设备在测量纺织材料芯吸能力方面的实用性。所构建的设备具有以下优点：提供自动化测量，最大限度地减少操作人员的主观误差；极快或长期测量；结果的数字记录；实验条件的一致性；使用水代替颜色的可能性，最后但并非最不重要的是，设备成本低。考虑到纺织材料芯吸能力测量的重要性和频繁性，以及所介绍的设备具有的优势，以及大多数现有设备都使用没有发布源代码的商业软件这一事实，我们相信，我们采用“开源”方法设计自动化软件和设备的想法将使科学家和工程师在使用或改进芯吸实验方面受益。
