Sökning: "Manual Defect Classification."

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Manual Defect Classification..

  1. 1. ML enhanced interpretation of failed test result

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Hiranmayi Pechetti; [2023]
    Nyckelord :Data Parsing; Machine Learning; Log file Analysis; Text Classification; Supervised Classification; Dataanalys; maskininlärning; loggfilsanalys; textklassificering; Övervakad klassificering;

    Sammanfattning : This master thesis addresses the problem of classifying test failures in Ericsson AB’s BAIT test framework, specifically distinguishing between environment faults and product faults. The project aims to automate the initial defect classification process, reducing manual work and facilitating faster debugging. LÄS MER

  3. 2. Defect classification in LPBF images using semi-supervised learning

    Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013); Karlstads universitet/Avdelningen för datavetenskap

    Författare :Anton Göransson; [2022]
    Nyckelord :Additive manufacturing; Laser powder bed fusion; Machine learning; Siamese neural networks; Deep learning; Defect classification;

    Sammanfattning : Laser powder bed fusion is an additive manufacturing technique that is capable of building metallic parts by spreading many layers of metal powder over a build surface and using a laser to melt specific sections of the surface. The part is built by melting consecutive layers on top of each other until the design is completed. LÄS MER

  4. 3. Detecting Defective Rail Joints on the Swiss Railways with Inception ResNet V2 : Simplifying Predictive Maintenance of Railway Infrastructure

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Anton Lu; [2022]
    Nyckelord :Rail Defect Detection; Rail Image Classification; Rail Joints; Object Detection; Image Classification; Deep Learning; Järnvägsfeldetektering; Järnvägsbildigenkänning; Järnvägsskarvar; Objectigenkänning; Bildklassificering; Djupinlärning;

    Sammanfattning : Manual investigation of railway infrastructure is a labor-intensive and time-consuming task, and automating it has become a high priority for railway operators to reduce unexpected infrastructure expenditure. In this thesis, we propose a new image classification approach for classifying defect and non-defective rail joints in image data, based on previous fault detection algorithms using object detection. LÄS MER

  5. 4. Application of Deep-learning Method to Surface Anomaly Detection

    Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

    Författare :Jiahui Le; [2021]
    Nyckelord :Image processing; Deep learning; Mask R-CNN; Surface anomaly detection; Surface defect detection;

    Sammanfattning : In traditional industrial manufacturing, due to the limitations of science and technology, manual inspection methods are still used to detect product surface defects. This method is slow and inefficient due to manual limitations and backward technology. LÄS MER

  7. 5. Developing a Simplified and Consistent Defect Taxonomy for Smaller Enterprises

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Johanna Iivanainen; [2021]
    Nyckelord :Defect Taxonomy; Defect Classification; Small- and Medium-sized Enterprises; Software Defects; Manual Defect Classification.; Defekttaxonomi; Defektklassifikation; Små och medelstora företag; Mjukvarufel; Manuell defektklassificering.;

    Sammanfattning : Developing software that meets the customers’ requirements, expectations, and quality standards is a challenging task for all software organizations. As modern software becomes more and more complex, so do the defects of the software. LÄS MER