Automated Surface Roughness Prediction from Scanning Electron Microscope Images using Convolutional Neural Network
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Date
2024-05-13
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Abstract
In recent years, the advent of Convolutional Neural Networks (CNNs) has opened up new
avenues for advancing Surface Roughness (SR) prediction methodologies, particularly
through the analysis of Scanning Electron Microscope (SEM) images. However, notable
gaps existed in the literature regarding the application of CNNs to SEM images for SR
prediction. This research addresses these existing gaps by employing CNN to analyze SEM
images for SR prediction, particularly focusing on the comparative analysis of different
magnification levels. Three distinct datasets, magnified at 150X, 250X, and 500X, were
utilized, comprising 2097, 2103, and 2102 images respectively. These images undergo
preprocessing techniques to enhance the CNN model's ability to generalize to new images.
Subsequently, a sequential CNN model, comprising 27 layers including convolutional, max
pooling, batch normalization, flatten, and fully connected dense layers, is developed and
trained on the datasets. The study provides detailed comparative analyses of accuracy,
precision, recall, and F1-score across magnification levels. Results indicate that the dataset
magnified at 500X consistently outperforms the others, exhibiting superior accuracy
(75.7%), precision (0.65), recall (0.72), and F1-score (0.72). This suggests that higher
magnification levels provide finer details and clearer images, enabling the model to discern
subtle features with increased accuracy. Additionally, the 500X dataset exhibits a better
balance between minimizing false positives and false negatives, making it more suitable
for real-world applications requiring detailed analysis of microscopic structures. These
findings underscore the importance of selecting appropriate magnification levels in SEM
imaging for accurate SR prediction.
Keywords: Surface Roughness; Surface Roughness Prediction; Scanning Electron
Microscope (SEM); Convolutional Neural Network (CNN); Magnified SEM Images