Computational Microstructure Analysis of Sintered Ceramics

2026/01/01 by Nikhil Dhanankam, Deeksha Kodangal, Rajendra K. Bordia +2
Computer Science · Engineering · Materials Science · Physics and Astronomy · #Advanced ceramic materials synthesis #Advanced materials and composites #Ceramic #Finite element method #Injection Molding Process and Properties #Microstructure #Sintering #cond-mat.mtrl-sci #cs.CE

paper · pdf · doi:10.1007/978-3-032-29909-3_47

published as In Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science - ICCS 2026 Workshops. Lecture Notes in Computer Science, vol 16787. Springer, Cham · Full Paper, ICCS 2026 Workshops

openalex publication_date 2026/01/01 · openalex created_date 2026/06/27 · openalex updated_date 2026/07/29 · arxiv created 2026/08/04 · arxiv updated 2026/08/06

Abstract

Characterizing materials through manual extraction of physical properties from microstructure images is a laborious process. This work presents a workflow to extract porosity, solid fraction, grain size distribution, and pore size distribution from scanning electron microscopy (SEM) images of sintered ceramic samples using an automated pipeline. The primary challenge for extracting physical properties from SEM images is the presence of unimodal histograms in SEM images as a result of the overlapping intensity ranges for the grain and pore phases. We evaluated several different methods for noise reduction and local thresholding of SEM images. We find that topological filtering in combination with Sauvola thresholding enables segmentation and extraction of physical property data from SEM images. We validated the automated pipeline by comparing our results with the results of manual analyses performed for samples sintered at 1200^oC and 1400^oC and achieved an Intersection over Union (IoU) score of 95.14% and 99.85%, respectively. The workflow provides an efficient means to automatically extract microstructure properties from SEM images as a crucial step in generating materials datasets for machine learning.

Citations

• Topological Filtering for 3D Microstructure Segmentation
• Which average, how many grains, and how to estimate robust confidence intervals in unimodal grain size populations
• High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Contradicting Geometrical Concepts in Pore Size Analysis Attained with Electron Microscopy and Mercury Intrusion
• Adaptive document image binarization
• Seeded region growing
• Nonlinear total variation based noise removal algorithms
• A fast two-dimensional median filtering algorithm
• THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1
• Survey over image thresholding techniques and quantitative performance evaluation
• A Threshold Selection Method from Gray-Level Histograms