Pakarinen, Janne

Pohja, Rami

Tahkola, Mikko

Zeb, Akhtar

English

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

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This document is downloaded from theVTT’s Research Information Portalhttps://cris.vtt.fiVTThttp://www.vtt.fiP.O. box 1000FI-02044 VTTFinlandBy using VTT’s Research Information Portal you are bound by thefollowing Terms & Conditions.I have read and I understand the following statement:This document is protected by copyright and other intellectualproperty rights, and duplication or sale of all or part of any of thisdocument is not permitted, except duplication for research use oreducational purposes in electronic or print form. You must obtainpermission for any other use. Electronic or print copies may not beoffered for sale.VTT Technical Research Centre of FinlandObject detection for the analysis of creep voids in high-temperaturemetallic structuresZeb, Akhtar; Tahkola, Mikko; Pohja, Rami; Pakarinen, JannePublished: 24/11/2021Link to publicationPlease cite the original version:Zeb, A., Tahkola, M., Pohja, R., & Pakarinen, J. (2021). Object detection for the analysis of creep voids in high-temperature metallic structures. Abstract from FCAI AI Day 2021, Finland.Download date: 13. Oct. 2022AI Day 2021Object detection for the analysis of creep voids in high-temperature metallic structuresAkhtar Zeb, Mikko Tahkola, Rami Pohja, Janne Pakarinen 12/01/2022 VTT – beyond the obviousProblem:Creep voids in metallic structures12/01/2022 VTT – beyond the obvious12/01/2022 VTT – beyond the obvious High-temperature metallic structures under load experience creep at temperature about 0.3 times the material’s melting temperature Creep means significant viscous time-dependent and liquid-like material flows in the direction of principal stress Prolonged creep condition leads to rupture and component failure At relatively early stages of creep, one important manifestation is the creep void at the grain boundaries of the material Reliable and accurate detection of creep void size and density helps in improving the life cycle management of high-temperature components Problem of creep void in metallic structures12/01/2022 VTT – beyond the obvious Creep void inspection in service conditions is usually performed by replica inspection Replica inspection samples are taken from components and inspected via optical and/or scanning electron microscope (SEM) However, interpretation of the results is often difficult and time-consuming ML can help in reducing human errors and speed up the process by analyzing large areas and multiple sample imagesCreep void analysis – traditional vs machine learning approachOur solution: YOLO object detection algorithm12/01/2022 VTT – beyond the obvious12/01/2022 VTT – beyond the obvious You Only Look Once Object detection algorithm popular for its speed, and outperformed, e.g., Sliding Window Object Detection, R CNN, Fast R CNN, Faster R CNN algorithms Built on PyTorch framework and tells about object class and object localization, i.e. bounding box Makes all the predictions in one forward pass  Yolo v1, Yolo v2, Yolo v3, Yolo v4, Yolo v5 YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics1open-source research into future vision AI methods We use the YOLOv5s, which is the smallest and fastest model We take advantage of pre-trained weights and default configuration of YOLOV5sYOLO algorithm1https://ultralytics.com/Source: https://github.com/ultralytics/yolov5Data:Scanning electron microscope images12/01/2022 VTT – beyond the obvious12/01/2022 VTT – beyond the obvious 100 SEM images of oxygen-free phosphorous doped copper sample surfaces With creep voids of different shapes and sizes Distinguishing creep voids from other damages could be challenging task even for the material expertScanning electron microscope imagesData pre-processing and annotation12/01/2022 VTT – beyond the obvious12/01/2022 VTT – beyond the obviousPre-processing images Formatting and cropping Resizing Splitting(height, width) (2304, 3072)(height, width) (2048, 3072)4x(height, width) (1024, 1536)Labelling and annotation CVAT – computer vision annotation tool Free, online, interactive video and image annotation tool for computer vision Supports several annotation formats (coco, yolo, etc.) Only one class “cv”, in this studyYolo format:Bounding box coordinates are in normalized xywh format (from 0 -1)Each row is class x_center y_center width height formatYOLO training, inference, and calculations12/01/2022 VTT – beyond the obvious12/01/2022 VTT – beyond the obvious Google Colab Images annotated using CVAT and exported in yolo format YOLOv5s model is trained using pretrained weights and default configuration and architectureTraining and resultsImages Epochs Training time mAP_0.564 100 45 min 0.8564 150 1 hour 0.83200 100 2 hours 0.8212/01/2022 VTT – beyond the obviousYOLOv5 inference –detections on full-size images 12/01/2022 VTT – beyond the obviousYOLOv5 inference – bounding boxes and creep void images12/01/2022 VTT – beyond the obviousCreep void calculations cv_area = π × width/2 × height/2 cv_num = len(cv_area) cv_frac_area = sum(cv_area)/image(w*h)*100width/2height/212/01/2022 VTT – beyond the obvious Total time taken by the algorithm starting from the initial pre-processing of the images to the saving of final results (Intel® Core™ i7-8665U Processor, 16 GB RAM) T_real is the system time in seconds from the starting point to the end of a process T_cpu is the sum of the system and user CPU time from the beginning to the end of a processInference timeConclusions12/01/2022 VTT – beyond the obvious12/01/2022 VTT – beyond the obvious YOLOv5 built on PyTorch framework Easy to train, e.g. using free Google Colab environment including GPUs Local installation is straightforward Cropped images of the creep voids are obtained, in addition to the detections on full-size images Detected all the creep voids correctly Bounding boxes coordinates enable calculation of desired parameters  Labelling accuracy would result in better inferences Creep void images can be further investigated using other tools, e.g. ImageJConclusions and future workFirst Name Surnamefirstname.surname@vtt.fi@VTTFinland@your_accountwww.vtt.fi

Object detection for the analysis of creep voids in high-temperature metallic structures

This document is downloaded from theVTT’s Research Information Portalhttps://cris.vtt.fiVTThttp://www.vtt.fiP.O. box 1000FI-02044 VTTFinlandBy using VTT’s Research Information Portal you are bound by thefollowing Terms & Conditions.I have read and I understand the following statement:This document is protected by copyright and other intellectualproperty rights, and duplication or sale of all or part of any of thisdocument is not permitted, except duplication for research use oreducational purposes in electronic or print form. You must obtainpermission for any other use. Electronic or print copies may not beoffered for sale.VTT Technical Research Centre of FinlandObject detection for the analysis of creep voids in high-temperaturemetallic structuresZeb, Akhtar; Tahkola, Mikko; Pohja, Rami; Pakarinen, JannePublished: 24/11/2021Link to publicationPlease cite the original version:Zeb, A., Tahkola, M., Pohja, R., & Pakarinen, J. (2021). Object detection for the analysis of creep voids in high-temperature metallic structures. Abstract from FCAI AI Day 2021, Finland.Download date: 19. Dec. 2021AI Day 2021Object detection for the analysis of creep voids in high-temperature metallic structuresAkhtar Zeb, Mikko Tahkola, Rami Pohja, Janne Pakarinen 24/11/2021 VTT – beyond the obviousProblem:Creep voids in metallic structures24/11/2021 VTT – beyond the obvious24/11/2021 VTT – beyond the obvious High-temperature metallic structures under load experience creep at temperature about 0.3 times the material’s melting temperature Creep means significant viscous time-dependent and liquid-like material flows in the direction of principal stress Prolonged creep condition leads to rupture and component failure At relatively early stages of creep, one important manifestation is the creep void at the grain boundaries of the material Reliable and accurate detection of creep void size and density helps in improving the life cycle management of high-temperature components Problem of creep void in metallic structures24/11/2021 VTT – beyond the obvious Creep void inspection in service conditions is usually performed by replica inspection Replica inspection samples are taken from components and inspected via optical and/or scanning electron microscope (SEM) However, interpretation of the results is often difficult and time-consuming ML can help in reducing human errors and speed up the process by analyzing large areas and multiple sample imagesCreep void analysis – traditional vs machine learning approachOur solution: YOLO object detection algorithm24/11/2021 VTT – beyond the obvious24/11/2021 VTT – beyond the obvious You Only Look Once Object detection algorithm popular for its speed, and outperformed, e.g., Sliding Window Object Detection, R CNN, Fast R CNN, Faster R CNN algorithms Built on PyTorch framework and tells about object class and object localization, i.e. bounding box Makes all the predictions in one forward pass  Yolo v1, Yolo v2, Yolo v3, Yolo v4, Yolo v5 YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics1open-source research into future vision AI methods We use the YOLOv5s, which is the smallest and fastest model We take advantage of pre-trained weights and default configuration of YOLOV5sYOLO algorithm1https://ultralytics.com/Source: https://github.com/ultralytics/yolov5Data:Scanning electron microscope images24/11/2021 VTT – beyond the obvious24/11/2021 VTT – beyond the obvious 100 SEM images of oxygen-free phosphorous doped copper sample surfaces With creep voids of different shapes and sizes Distinguishing creep voids from other damages could be challenging task even for the material expertScanning electron microscope imagesData pre-processing and annotation24/11/2021 VTT – beyond the obvious24/11/2021 VTT – beyond the obviousPre-processing images Formatting and cropping Resizing Splitting(height, width) (2304, 3072)(height, width) (2048, 3072)4x(height, width) (1024, 1536)Labelling and annotation CVAT – computer vision annotation tool Free, online, interactive video and image annotation tool for computer vision Supports several annotation formats (coco, yolo, etc.) Only one class “cv”, in this studyYolo format:Bounding box coordinates are in normalized xywh format (from 0 -1)Each row is class x_center y_center width height formatYOLO training, inference, and calculations24/11/2021 VTT – beyond the obvious24/11/2021 VTT – beyond the obvious Google Colab Images annotated using CVAT and exported in yolo format YOLOv5s model is trained using pretrained weights and default configuration and architectureTraining and resultsImages Epochs Training time mAP_0.564 100 45 min 0.8564 150 1 hour 0.83200 100 2 hours 0.8224/11/2021 VTT – beyond the obviousYOLOv5 inference –detections on full-size images 24/11/2021 VTT – beyond the obviousYOLOv5 inference – bounding boxes and creep void images24/11/2021 VTT – beyond the obviousCreep void calculations cv_area = π × width/2 × height/2 cv_num = len(cv_area) cv_frac_area = sum(cv_area)/image(w*h)*100width/2height/224/11/2021 VTT – beyond the obvious Total time taken by the algorithm starting from the initial pre-processing of the images to the saving of final results (Intel® Core™ i7-8665U Processor, 16 GB RAM) T_real is the system time in seconds from the starting point to the end of a process T_cpu is the sum of the system and user CPU time from the beginning to the end of a processInference timeConclusions24/11/2021 VTT – beyond the obvious24/11/2021 VTT – beyond the obvious YOLOv5 built on PyTorch framework Easy to train, e.g. using free Google Colab environment including GPUs Local installation is straightforward Cropped images of the creep voids are obtained, in addition to the detections on full-size images Detected all the creep voids correctly Bounding boxes coordinates enable calculation of desired parameters  Labelling accuracy would result in better inferences Creep void images can be further investigated using other tools, e.g. ImageJConclusions and future workFirst Name Surnamefirstname.surname@vtt.fi+358 1234 5678@VTTFinland@your_accountwww.vtt.fi

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Object detection for the analysis of creep voids in high-temperature metallic structures

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