Coeus: A Universal Search Engine for Additive Manufacturing
Coeus: A Universal Search Engine for Additive Manufacturing
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DOI：
10.1109/access.2023.3271890
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Lars Folkerts;Nicholas Kater;N. G. Tsoutsos
中科院分区：
文献类型：
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作者：
Lars Folkerts;Nicholas Kater;N. G. Tsoutsos
加法制造已经变得越来越流行，并为增加协作、快速制造周转时间和快速原型制造打开了大门。3D模型数据库提供了新的协作机会，可帮助用户根据请求查找、修改和制造零件。然而，当前的过程严重依赖于用户定义的基于文本的标签来描述和识别部件，然而用户标记是一个昂贵且费力的过程。为了解决传统的基于标签的搜索方法的局限性，这项工作提出了新的基于形状的搜索技术，在现有技术的基础上带来了显著的可用性改进。特别是，我们的方法允许用户在制造过程的不同阶段查询零件，包括近似模型、gcode打印机文件和现实世界对象。我们技术的核心是一个生成性对抗网络，它将3D形状展平为基于深度的2.5D图像，然后基于频域表示和位置敏感散列方案对这些图像进行分类和查询。我们使用丰富的日常对象数据集来评估我们的方法，我们的评估结果报告了我们测试集的高精度检索率。
Additive manufacturing has become increasingly popular and is opening doors for increased collaboration, quick manufacturing turnaround times, and rapid prototyping. New collaboration opportunities are enabled by 3D model databases that help users find, modify and manufacture parts upon request. Nevertheless, the current process relies heavily on user-defined text-based labels to describe and identify parts, yet user tagging is an expensive and laborious process. To address the limitations of traditional tag-based methods, this work proposes new shape-based search techniques that bring significant usability improvements over the current state of the art. In particular, our approach allows users to query for parts at different stages of the manufacturing process, including approximate models, GCode printer files, and real world objects. At the core of our technique is a generative adversarial network that flattens 3D shapes into depth-based 2.5D images, which are then cataloged and queried based on a frequency-domain representation and a locality-sensitive hashing scheme. We evaluate our methodology using a rich dataset of everyday objects, and our evaluation results report a high accuracy retrieval rate for our test set.