{"title":"Dataguzzler-Python and SpatialNDE2: Crucial Software Infrastructure for Reconfigurable NDE Data Acquisition With Spatial Context","authors":"Tyler J. Lesthaeghe;Stephen D. Holland","doi":"10.1109/OJIM.2024.3459989","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"3 ","pages":"1-9"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680071","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Instrumentation and Measurement","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10680071/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.