Deep learning based automated quantification of powders used in additive manufacturing

IF 4.2 Q2 ENGINEERING, MANUFACTURING Additive manufacturing letters Pub Date : 2024-09-14 DOI:10.1016/j.addlet.2024.100241
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Abstract

This study proposes a novel deep learning technique for efficient powder morphology characterization, crucial for successful additive manufacturing. The method segments powder particles in microscopy images using Pix2Pix image translation model, enabling precise quantification of size distribution and extraction of critical morphology parameters like circularity and aspect ratio. The proposed approach achieves high accuracy (Structural Similarity Index of 0.8) and closely matches established methods like laser diffraction in measuring particle size distribution (within a deviation of ∼7 %) and allows determination of additional particle attributes of aspect ratio and circualarity in a reliable, repeated, and automated way. These findings highlight the potential of deep learning for automated powder characterization, offering significant benefits for optimizing additive manufacturing processes.

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基于深度学习的增材制造所用粉末自动定量分析
本研究提出了一种新颖的深度学习技术,用于高效的粉末形态表征,这对成功的增材制造至关重要。该方法使用 Pix2Pix 图像转换模型对显微镜图像中的粉末颗粒进行分割,从而能够精确量化粒度分布并提取圆度和长宽比等关键形态参数。所提出的方法实现了高精度(结构相似度指数为 0.8),在测量粒度分布方面与激光衍射等成熟方法非常接近(偏差在 ∼ 7 % 范围内),并能以可靠、重复和自动化的方式确定长宽比和圆度等其他颗粒属性。这些发现凸显了深度学习在自动粉末表征方面的潜力,为优化增材制造工艺提供了显著优势。
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来源期刊
Additive manufacturing letters
Additive manufacturing letters Materials Science (General), Industrial and Manufacturing Engineering, Mechanics of Materials
CiteScore
3.70
自引率
0.00%
发文量
0
审稿时长
37 days
期刊最新文献
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