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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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    <subfield code="a">Additive manufacturing</subfield>
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    <subfield code="u">Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom</subfield>
    <subfield code="a">Zidong Wang</subfield>
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    <subfield code="a">Lulu Tian</subfield>
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    <subfield code="a">Stanislao Lauria</subfield>
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    <subfield code="a">Xiaohui Liu</subfield>
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    <subfield code="u">Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom</subfield>
    <subfield code="a">Weibo Liu</subfield>
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    <subfield code="a">Melt pool segmentation for additive manufacturing: A generative adversarial network approach</subfield>
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    <subfield code="a">&lt;p&gt;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.&lt;br&gt;
To 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.&lt;/p&gt;</subfield>
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