Abstract
Metallic high-temperature structures are subject to creep. Creep means significant viscous time-dependent and liquid-like material flow in the direction of the principal stress leading eventually to component failure. One important manifestation of creep is the voids at the grain boundaries of the material. The reliable and accurate detection of creep void density and the size of voids is an important step to improve the high-temperature component lifecycle management.
Usually creep void analysis in service conditions is performed by replica inspection. The interpretation of the results can often be difficult and time-consuming. Large number of voids makes density and size analysis challenging and increases possibility of human misinterpretation. We show that AI-based creep void detection removes these obstacles by quickly processing large number of sample images with high detection accuracy.
Computer vision (CV), a field of artificial intelligence (AI), has been utilized in industries ranging from energy to manufacturing to, e.g., detect issues in products and processes based on image and video data. Object detection aspect of CV focuses on detection and identification of objects in an image or video, such as face detection and damage identification in machines.
We applied the YOLOv5s model with its default configuration and pretrained weights on scanning electron microscope (SEM) images of oxygen-free phosphorus-doped copper sample surfaces containing creep voids. Each of the 40 high-resolution SEM images was split into four smaller images and annotated with online tool CVAT. The model was trained using 160 images, and satisfactory mean average precision of 0.82 was achieved. By adjusting the confidence threshold, the model predicts all the creep voids in test images correctly. The trained model outputs coordinates of the voids in the images and cropped images that contain only the voids, which can be used to calculate the void density and sizes.
Usually creep void analysis in service conditions is performed by replica inspection. The interpretation of the results can often be difficult and time-consuming. Large number of voids makes density and size analysis challenging and increases possibility of human misinterpretation. We show that AI-based creep void detection removes these obstacles by quickly processing large number of sample images with high detection accuracy.
Computer vision (CV), a field of artificial intelligence (AI), has been utilized in industries ranging from energy to manufacturing to, e.g., detect issues in products and processes based on image and video data. Object detection aspect of CV focuses on detection and identification of objects in an image or video, such as face detection and damage identification in machines.
We applied the YOLOv5s model with its default configuration and pretrained weights on scanning electron microscope (SEM) images of oxygen-free phosphorus-doped copper sample surfaces containing creep voids. Each of the 40 high-resolution SEM images was split into four smaller images and annotated with online tool CVAT. The model was trained using 160 images, and satisfactory mean average precision of 0.82 was achieved. By adjusting the confidence threshold, the model predicts all the creep voids in test images correctly. The trained model outputs coordinates of the voids in the images and cropped images that contain only the voids, which can be used to calculate the void density and sizes.
| Original language | English |
|---|---|
| Publication status | Published - 24 Nov 2021 |
| MoE publication type | Not Eligible |
| Event | FCAI AI Day 2021 - Espoo / online, Finland Duration: 4 Nov 2021 → 4 Nov 2021 https://fcai.fi/ai-day-2021 |
Conference
| Conference | FCAI AI Day 2021 |
|---|---|
| Country/Territory | Finland |
| Period | 4/11/21 → 4/11/21 |
| Internet address |
Keywords
- Artificial Intelligence (AI)
- Computer vision
- Object detection
- Creep
- High temperature metals
- Metallic structures