Advanced Semantic Segmentation of Cellular Substructure in Selectively Laser Melted 316L Stainless Steel

Paper in proceedings (conference paper)

en

In material science, especially when analyzing SEM and TEM images, the measurement of microstructural features has traditionally relied on manual methods. These methods are slow and prone to human error. A framework employing the BIRCH clustering algorithm and thresholding techniques was developed to automatically segment and categorize microstructural features. The framework can extract quantitative parameters such as cell diameter, aspect ratio, major and minor axis lengths, etc. While cell diameter can be measured using traditional methods like the line-intercept technique, obtaining other parameters manually is significantly more difficult. By utilizing the automated framework, results are more consistent. However, while this framework substantially reduces manual effort and accelerates analysis, challenges remain when addressing highly complex or anomalous images. In such cases, artificial neural networks (ANN) offer a more adaptive and robust solution. Yet, preparing the extensive well-labeled datasets required for ANN is time-consuming and resource-intensive. Semi-automated data preparation strategies are being explored, to minimize these demands by reducing manual input and enhancing the efficiency and scalability of the analysis pipeline. In this work, the cellular structure present in additively manufactured 316L steel was evaluated using the above-mentioned tools to enable potential correlation of the microstructural features with mechanical properties.

Addtive manufacturing; Machine learning; austenitic steel 316L; Dislocation substructure; Deep learning

2025-12-08

Elsevier BV

Procedia Structural Integrity

106–113

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