Flow visualization by Matlab® based image analysis of high-speed polymer melt extrusion film casting process for determining necking defect and quantifying surface velocity profiles

IF 9.9 Q1 MATERIALS SCIENCE, COMPOSITES Advanced Industrial and Engineering Polymer Research Pub Date : 2022-01-01 DOI:10.1016/j.aiepr.2021.02.003
Aarati Vagga , Swapnil Aherrao , Harshawardhan Pol , Vivek Borkar
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引用次数: 3

Abstract

The primary objective of this research paper is to detect and quantify the necking defect and surface velocity profiles in high-speed polymer melt extrusion film casting (EFC) process using Matlab® based image processing techniques. Extrusion film casting is an industrially important manufacturing process and is used on an industrial scale to produce thousands of kilograms of polymer films/sheets and coated products. In this research, the necking defect in an EFC process has been studied experimentally and the effects of macromolecular architecture such as long chain branching (LCB) on the extent of necking have been determined using image processing methodology. The methodology is based on the analysis of a sequence of image frames taken with the help of a commercial CCD camera over a specific target area of the EFC process. The image sequence is then analyzed using Matlab® based image processing toolbox wherein a customized algorithm is written and executed to determine the edges of the extruded molten polymeric film to quantify the necking defect. Alongwith the necking defect, particle tracking velocimetry (PTV) technique is also used in conjunction with the Matlab® software to determine the centerline and transverse velocity profiles in the extruded molten film. It is concluded from this study that image processing techniques provide valuable insights into quantifying both the necking defect and the associated velocity profiles in the molten extruded film.

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基于Matlab®的高速聚合物熔体挤出膜铸造过程流场可视化图像分析,用于确定颈缩缺陷和量化表面速度分布
本研究论文的主要目的是利用基于Matlab®的图像处理技术检测和量化高速聚合物熔体挤出膜铸造(EFC)过程中的颈缩缺陷和表面速度分布。挤出薄膜铸造是工业上重要的制造工艺,在工业规模上用于生产数千公斤的聚合物薄膜/片材和涂层产品。在本研究中,实验研究了EFC过程中的颈缩缺陷,并利用图像处理方法确定了大分子结构(如长链分支(LCB))对颈缩程度的影响。该方法是基于在EFC过程的特定目标区域的商业CCD相机的帮助下对一系列图像帧的分析。然后使用基于Matlab®的图像处理工具箱对图像序列进行分析,其中编写并执行定制算法,以确定挤出熔融聚合物薄膜的边缘,以量化颈缩缺陷。随着颈缩缺陷,颗粒跟踪测速(PTV)技术也与Matlab®软件一起使用,以确定挤出熔融膜中的中心线和横向速度剖面。从这项研究中得出结论,图像处理技术为量化颈缩缺陷和熔融挤压膜中相关的速度分布提供了有价值的见解。
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来源期刊
Advanced Industrial and Engineering Polymer Research
Advanced Industrial and Engineering Polymer Research Materials Science-Polymers and Plastics
CiteScore
26.30
自引率
0.00%
发文量
38
审稿时长
29 days
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