Enhancing the accuracy and efficiency of characterizing polymeric cellular structures using 3D-based computed tomography

Characterizing the morphology of polymeric foams is crucial for determining their practical applicability. The internal cellular structure of polymeric foams is typically analyzed by 2 D imaging techniques, such as Scanning Electron Microscopy (SEM) and optical microscopy. The problem with these techniques is that their tests are tedious, destructive, and the accuracy of the obtained results is questionable. The objective of this paper is to establish and experimentally verify an efficient 3- dimensional (3 D) Microcomputed-tomography based methodology for reliably estimating and characterizing each of the phases commonly present in multiple types of polymeric foam samples, such as the open, the closed, and the solid phase. A comparative study was carried out between morphology data obtained from 2-dimensional (2 D) analysis and those obtained from 3 D analysis to investigate the reliability of the 2 D analysis results. In this context, the experimental results revealed that by using a 2 D method the open porosity was underestimated at the expense of closed porosity, which in turn was overestimated, while the total porosity was not impacted. Also, visualization of the internal structure of polymer foams by using Micro-CT provides details about the 3 D space which cannot be obtained from SEM images. The analysis of foamed specimen demonstrated that the polymeric foam phases extracted from Micro-CT images were in agreement with the experimentally measured values of total porosity of the samples. In an effort to reduce computational requirements, the effects of reducing data size on the accuracy of results has also been studied by averaging image pixels in 3 D space and the results were compared for multiple types of foam structures. This method reduced the processing time considerably, and yielded comparable porosity values. However, the number of detected pores were lowered due to the inability of this method to detect very small cells after 3 D averaging of image pixels.