Dataguzzler-Python 和 SpatialNDE2:利用空间上下文进行可重构无损检测数据采集的关键软件基础设施

Tyler J. Lesthaeghe;Stephen D. Holland
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摘要

在无损检测(NDE)领域,我们有时需要一个由多个执行器和传感器组成的复杂系统来测量和评估试样的材料状况或结构完整性。对于涉及多重现象或几何建模的更先进技术而言,复杂的系统尤为必要。在研究实验室中,我们很少能预先了解测量的复杂性,我们需要根据需要灵活地重新配置测量系统。软件是连接测量系统的粘合剂。传统的临时软件方法在现代环境中很快就难以为继。我们提出了一种替代方法,以满足现代无损检测实验室对敏捷性的需求:一种可重新配置的模块化软件架构,从底层开始构建,以适应数据管理、自动化、并行性、几何和机器人技术以及版本控制等领域相互冲突的要求。我们介绍了一对新的开源工具 Dataguzzler-Python 和 SpatialNDE2,它们有助于无损检测实验室的仪器控制、数据采集和处理。这两个工具组成了一个框架,提供以下功能:仪器的多路自动和手动控制、存储所获数据的版本数据库、并行采集和实时高性能/GPU 计算、在几何上下文中采集和存储数据的能力,以及对所获数据进行可视化和交互的能力。本文将讨论其设计、实施以及在无损检测实验室中使用的初步经验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dataguzzler-Python and SpatialNDE2: Crucial Software Infrastructure for Reconfigurable NDE Data Acquisition With Spatial Context
In the field of nondestructive evaluation (NDE), we sometimes need an intricate system of multiple actuators and sensors to measure and assess the material condition or structural integrity of a specimen. Complicated systems are especially necessary for more advanced techniques that involve multiple phenomena or modeling in a geometric context. In the research laboratory, we rarely understand the intricacies of the measurement up front, and we need the agility to reconfigure our measurement system as needs evolve. Software is the glue that ties our measurement systems together. The traditional approach of ad hoc software quickly becomes unsustainable in the modern environment. We propose an alternative approach that addresses the need for agility in the modern NDE laboratory: a reconfigurable, modular software architecture that is built from the ground up to accommodate conflicting requirements in the areas of data management, automation, parallelism, geometry and robotics, and version control. We describe a new pair of open-source tools, Dataguzzler-Python and SpatialNDE2, that facilitate instrumentation control, data acquisition, and processing for the NDE laboratory. The tools make up a framework that provides the following: multiplexed automatic and manual control of instrumentation, a versioned database to store the acquired data, parallel acquisition and live high performance/GPU computation, the ability to acquire and store data in geometric context, and the ability to visualize and interact with the acquired data. This article discusses their design, implementation, and initial experiences in using them in the NDE laboratory.
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