基于软件的钻头三维扫描技术与应用进展

Crispin Chatar, Kishore Mulchandani
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引用次数: 0

摘要

创建现实世界设备的数字化身的能力打开了创建各种级别的数字和虚拟双胞胎的可能性。将这些数据与实时数据相结合,可以成为了解生命周期、跟踪操作和收集数据以预测设备健康状况的强大工具。我们一直在测试新的软件方法,以便利用现有技术为设备生成虚拟形象。虽然许多公司也在用复杂的硬件做这件事,但我们一直在使用新的软件方法,以便硬件需求可以像普通的手机一样简单。我们已将这些技术应用于钻头。其结果是一个创建钻头三维重建模型的应用程序。这创造了一个钻头的化身,可以用于许多用途,包括设备跟踪和数据提取。演示了三维重建和简单线性管道的自动化结果,该管道将位视频转换为三维模型。将效果图与同一地点的照片进行比较,结果几乎无法区分。这些模型可以用于虚拟双胞胎的生成。在钻头的使用寿命期间进行多次扫描,将使人们能够以一种新的方式思考虚拟双胞胎。一个例子是能够及时更新快照模型,并使用人工智能来推断钻头的寿命。这些模型还可以用于运行额外的分析,因为模型可以注入一些上下文信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Software-Based Three-Dimensional Scan of a Drill Bit; Advances in Technology and Applications
The ability to create a digital avatar of real-world equipment opens the possibility to create various levels of digital and virtual twins. Pairing these with real-time data can be a powerful tool to understand the life cycle, track operations, and collect data to predict the health of equipment. We have been testing new software methods to enable the use of existing technology to generate avatars for equipment. While many companies are also doing this with complex hardware, we have been using new software methods so that hardware requirements could be as simple as a common cell phone. We have applied these techniques to drill bits. The result is an application that creates a three-dimensional reconstructed model of a bit. This creates an avatar of the drilling bit that can be used for many purposes including equipment tracking and data extraction. Results from the three-dimensional reconstruction and the automating of a simple linear pipeline that converts bit videos to three-dimensional models is demonstrated. The renderings were compared to photos at the same locations and the results were virtually indistinguishable. The models can then be used for virtual twin generation. Multiple scans over the lifespan of the drill bit will allow access to a new way of thinking about virtual twins. One example is the ability to update a model with a snapshot in time and use AI to infer the life of the bit. These models can also be used to run additional analysis since the model can be infused with some contextual information.
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