计算机视觉工具包用于工厂车间人工制品的非侵入性监测
Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts
摘要：
数字化使智能互联技术成为企业、政府和社区不可或缺的一部分。对于制造业数字化，一直在积极研究和开发，重点是云制造（CM）和工业物联网（IIoT）。这项工作提出了一个计算机视觉工具包（CV工具包）的工厂车间的非侵入式数字化符合工业4.0的工厂数据汇集的要求。目前，由于其设计和传感器的变化受到限制，传统系统的数字化仍然存在技术挑战。这种新型工具包的开发旨在促进传统生产机械和工厂车间工件与数字和智能制造环境的轻松集成，而无需对机器进行任何物理更改。开发的系统是模块化的，可以实时监控生产机械。模块化方面允许在CV Toolkit的当前框架中纳入新的软件应用程序。为了以简单、可部署和具有成本效益的方式将该工具包与制造车间连接起来，该工具包与已知的制造数据标准MTConnect集成，以将数字输入“转换”成可由商业状态跟踪和报告软件解决方案读取的数据流。拟议的工具包演示使用模拟面板环境开发的内部在辛辛那提大学，以突出其可用性。
英文摘要：
Digitization has led to smart, connected technologies be an integral part of businesses, governments and communities. For manufacturing digitization, there has been active research and development with a focus on Cloud Manufacturing (CM) and the Industrial Internet of Things (IIoT). This work presents a computer vision toolkit (CV Toolkit) for non-invasive digitization of the factory floor in line with Industry 4.0 requirements for factory data collection. Currently, technical challenges persist towards digitization of legacy systems due to the limitation for changes in their design and sensors. This novel toolkit is developed to facilitate easy integration of legacy production machinery and factory floor artifacts with the digital and smart manufacturing environment with no requirement of any physical changes in the machines. The system developed is modular, and allows real-time monitoring of production machinery. Modularity aspect allows the incorporation of new software applications in the current framework of CV Toolkit. To allow connectivity of this toolkit with manufacturing floors in a simple, deployable and cost-effective manner, the toolkit is integrated with a known manufacturing data standard, MTConnect, to "translate" the digital inputs into data streams that can be read by commercial status tracking and reporting software solutions. The proposed toolkit is demonstrated using a mock-panel environment developed in house at the University of Cincinnati to highlight its usability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
