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Melt pool segmentation for additive manufacturing: A generative adversarial network approach

Weibo Liu; Zidong Wang; Lulu Tian; Stanislao Lauria; Xiaohui Liu


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Additive manufacturing (AM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the AM process, it is of critical importance to obtain high-quality thermal images by using image processing techniques. In this paper, a novel image processing method is put forward with aim to improve the contrast ratio of the thermal images for image segmentation.<br>\nTo be specific, an image-enhancement generative adversarial network (IEGAN) is developed, where a new objective function is designed for the training process. To verify the superiority and feasibility of the proposed IEGAN, the thermal images captured from an AM process are utilized for image segmentation. Experiment results demonstrate that the developed IEGAN outperforms the original GAN in improving the contrast ratio of the thermal images.</p>", 
  "license": "", 
  "creator": [
    {
      "affiliation": "Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom", 
      "@type": "Person", 
      "name": "Weibo Liu"
    }, 
    {
      "affiliation": "Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom", 
      "@type": "Person", 
      "name": "Zidong Wang"
    }, 
    {
      "affiliation": "School of Automation Engineering, University of Electronic Science and Technology of China, Sichuan 611731, China", 
      "@type": "Person", 
      "name": "Lulu Tian"
    }, 
    {
      "affiliation": "Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom", 
      "@type": "Person", 
      "name": "Stanislao Lauria"
    }, 
    {
      "affiliation": "Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom", 
      "@type": "Person", 
      "name": "Xiaohui Liu"
    }
  ], 
  "headline": "Melt pool segmentation for additive manufacturing: A generative adversarial network approach", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2021-05-05", 
  "url": "https://zenodo.org/record/6226688", 
  "keywords": [
    "Additive manufacturing", 
    "Generative adversarial network", 
    "Defect detection", 
    "Image processing", 
    "Image segmentation", 
    "Thermal image"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1016/j.compeleceng.2021.107183", 
  "@id": "https://doi.org/10.1016/j.compeleceng.2021.107183", 
  "@type": "ScholarlyArticle", 
  "name": "Melt pool segmentation for additive manufacturing: A generative adversarial network approach"
}
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