{"id":22646960,"url":"https://github.com/sayansomya/defect-detection-in-3d-printing","last_synced_at":"2025-08-17T21:34:24.328Z","repository":{"id":263497765,"uuid":"890581768","full_name":"SayanSomya/Defect-Detection-in-3D-Printing","owner":"SayanSomya","description":"Bachelors Thesis Project for Defect Detection ","archived":false,"fork":false,"pushed_at":"2024-11-18T21:32:02.000Z","size":37,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-29T06:47:20.207Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SayanSomya.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-18T20:28:17.000Z","updated_at":"2024-11-18T21:32:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"8600b2f4-5b60-4b33-b764-0850229c8bb5","html_url":"https://github.com/SayanSomya/Defect-Detection-in-3D-Printing","commit_stats":null,"previous_names":["sayansomya/defect-detection-in-3d-printing"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SayanSomya/Defect-Detection-in-3D-Printing","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayanSomya%2FDefect-Detection-in-3D-Printing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayanSomya%2FDefect-Detection-in-3D-Printing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayanSomya%2FDefect-Detection-in-3D-Printing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayanSomya%2FDefect-Detection-in-3D-Printing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SayanSomya","download_url":"https://codeload.github.com/SayanSomya/Defect-Detection-in-3D-Printing/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayanSomya%2FDefect-Detection-in-3D-Printing/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270912461,"owners_count":24666737,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-17T02:00:09.016Z","response_time":129,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-09T07:31:46.469Z","updated_at":"2025-08-17T21:34:24.245Z","avatar_url":"https://github.com/SayanSomya.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Defect Detection in 3D Printing\n\u003cb\u003eBachelor's Thesis Project\u003c/b\u003e\n\nWelcome to the official repository for Defect Detection in 3D Printing, a Bachelor's Thesis project focused on leveraging advanced object detection models (YOLOv5 and YOLOv11) to identify defects in 3D-printed objects. This project combines computer vision, machine learning, and domain-specific problem-solving to enhance the quality control process in additive manufacturing.\n\n## Project Overview\n\nAdditive manufacturing, or 3D printing, has revolutionized how we create objects by enabling complex, highly customizable designs. However, the quality assurance of 3D-printed components remains a significant challenge due to defects that may occur during printing. This project addresses this challenge by implementing state-of-the-art object detection models to automate defect detection, thereby reducing the reliance on manual inspection.\n\nKey Features:\n1. \u003cb\u003eAutomated Defect Detection:\u003c/b\u003e Identify common defects such as layer shifts, stringing, and under-extrusion.\n2. \u003cb\u003eYOLO-based Object Detection Models:\u003c/b\u003e Evaluate and compare YOLOv5 and YOLOv11 for detecting anomalies in 3D prints.\n3. \u003cb\u003eCustom Dataset:\u003c/b\u003e Built a comprehensive dataset containing annotated defect images for model training and testing.\n4. \u003cb\u003ePerformance Optimization:\u003c/b\u003e Focused on precision, recall, and inference time to ensure practical usability in real-time applications.\n\n## Methodology\n1. \u003cb\u003eDataset Preparation:\u003c/b\u003e Labeled the dataset with bounding boxes for precise defect localization \u0026 performed image augmentation to improve model generalization.\n2. \u003cb\u003eModel Training:\u003c/b\u003e Implemented YOLOv5 and YOLOv11 in Google Collab and optimized hyperparameters for better accuracy and inference speed.\n3. \u003cb\u003eEvaluation:\u003c/b\u003e Compared YOLOv5 and YOLOv11 based on precision, recall, F1-score, and mAP (Mean Average Precision).\n\n## Future Work\n1. Extend the dataset with more defect types and variations.\n2. Explore other state-of-the-art object detection architectures like YOLOv11 and DETR.\n3. Integrate the detection system into a 3D printing pipeline for real-time defect monitoring.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayansomya%2Fdefect-detection-in-3d-printing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayansomya%2Fdefect-detection-in-3d-printing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayansomya%2Fdefect-detection-in-3d-printing/lists"}