Geometric deep learning specialist物理has launched what it is calling the world’s most powerful geometric search engine –Thangs。
Thangs不用扫描文本或图像，而是使用深度学习算法来根据多边形或三角形索引3D模型，这些模型构成了它们的体积。在发布会上，免费加入站点已经拥有超过一百万个公共3D型号的索引，并计划在未来几个月内进行扩展。
除了智能搜索算法外，Thangs还为3D社区提供版本控制功能和“兼容零件预测”，并有望受到设计师，工程师和3D爱好者的热门歌曲。
保罗·鲍尔斯（Paul Powers）, CEO of Physna, explains: “Thangs is Physna’s first open product, but it uses some of the same powerful technology at the core of our enterprise offerings. Our enterprise product is widely recognized as the technological leader in 3D search and analysis, and we’re democratizing some of its capabilities through Thangs. This product is built to be powerful enough for a leading aerospace CAD engineer, yet simple enough for literally anyone to use.
3D模型的Google X GitHub
Physna称其产品为3D世界的Google Xgithub跨越并查看项目的领导团队，不难看出原因。Physna的CTO Dennis DeMeyere是Google Cloud CTO办公室的前技术总监。7月，Github现任首席技术官Jason Warner也加入了公司董事会成员。借助全明星阵容，PhysNA的目标是将这两个技术巨头的主要功能功能纳入其自己的3D模型平台。
Thangs建立在以下前提下，与传统软件开发相比，3D数据界仍然是一个高度手动的。没有中央搜索引擎，因此找到所需的文件是一个繁琐的过程。由于缺乏版本控制软件，公司内的协作工作也是基本的，并且可能涉及云存储或通过电子邮件共享。
How does it work?
With Thangs, users are able to upload parts and receive suggestions for where that part may be used and what commercially available components may be compatible with it. There is, of course, a traditional text-based search box to find desired parts as well. Users can search based on the object’s physical properties, measurements, and features and receive predictions about its function, cost, materials, and performance.
The site also functions in a social capacity, as designers and colleagues are able to share and collaborate on 3D models seamlessly. Version control is automated – much like GitHub – and users are able to leave comments and ‘like’ models to save them for later, which triggers a notification for the uploader. Since a 3D designer’s portfolio of work is accessible from their profile, it can also serve as a resume of sorts.
根据Powers的说法，Thangs有望在推出后一年内成为世界上最大的3D模型数据库。它索引了它自动找到的任何公共模型，并且基于深度学习算法，每次添加都变得越来越复杂。
随着3D打印技术的进步，添加剂制造中的机器学习变得越来越普遍。本月早些时候工程公司Renishaw与机器人技术专家合作加性自动化推进自动化后处理技术对于金属3D打印零件。该协作将涉及使用深度学习算法自动使用机器人臂检测和删除支撑结构。
Elsewhere, researchers fromArgonne国家实验室和德克萨斯农工大学have developed an innovative new基于机器学习的缺陷检测方法在3D打印零件中。借助实时温度数据，科学家能够在激光PBF期间的热史与地下缺陷的形成之间建立相关性。现在，团队计划使用更多的数据集和改进的机器学习模型来开发工作。
第四届年度3D印刷行业奖将于2020年11月举行，我们需要奖杯。有机会赢得全新CraftBot Flow IDEX XL3D打印机，在此处输入Myminifactory奖杯设计比赛。我们很乐意接受提交，直到2020年9月30日。
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特色图片显示Physna的Thangs UI。通过Physna图像。
