Deep Learning and Machine Learning Approaches for Non Contact Surface Roughness Prediction from Scanning Electron Microscope (SEM) Images
| dc.contributor.author | Maria Akter Luthfa | |
| dc.date.accessioned | 2025-11-23T06:41:37Z | |
| dc.date.available | 2025-11-23T06:41:37Z | |
| dc.date.issued | 2025-08-14 | |
| dc.description.abstract | Surface roughness is crucial to the quality, functionality, and durability of manufactured parts. Nonetheless, there is a problem that conventional methods of contact measurements are costly, time-consuming, and ineffective in analyzing complicated surface geometry. To address these constraints, this work proposes deep and traditional machine learning models to specifically predict surface roughness using Scanning Electron Microscope (SEM) images, eliminating the need for physical probing or handcrafted surface analysis. Titanium alloys as a target material were chosen because of the wide range of essential applications, and SEM images were obtained at various magnification levels to capture the broad range of patterns on their surface. A custom design Convolutional Neural Network (CNN) was constructed and trained to perform regression tasks, and two pre-trained deep learning models, ResNet50 and VGG16, were also used. Simultaneously, the images were used to extract the handcrafted features, which were in turn trained by the conventional machine learning models, such as Support Vector Regression (SVR), Random Forest, and Linear Regression. The findings showed that deep learning models mostly performed better, especially VGG16, than the traditional models by providing higher accuracy and R2 values above 0.95 in the majority of cases. The developed approach has excellent prospects for application in the real world to high-precision industries, including aerospace, biomedical, and electronic manufacturing sectors, allowing for improved performance control and process optimization speed. Keywords: Surface Roughness; Surface Roughness Prediction; Scanning Electron Microscope; Convolutional Neural Network; Support Vector Regression; Random Forest; Linear Regression. | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/147 | |
| dc.title | Deep Learning and Machine Learning Approaches for Non Contact Surface Roughness Prediction from Scanning Electron Microscope (SEM) Images |