首页> 外文会议>SAMPE Long Beach Conference and Exhibition >CONTEXT AWARE COMPUTING LEVERAGES THE INDUSTRIAL INTERNET OF THINGS (IIOT) TO CREATE A RICH DIGITAL CONTEXT AND WEAVE THE DIGITAL THREAD FOR AUTOMATED AND OPTIMIZED DECISION MAKING IN COMPOSITES MANUFACTURING
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CONTEXT AWARE COMPUTING LEVERAGES THE INDUSTRIAL INTERNET OF THINGS (IIOT) TO CREATE A RICH DIGITAL CONTEXT AND WEAVE THE DIGITAL THREAD FOR AUTOMATED AND OPTIMIZED DECISION MAKING IN COMPOSITES MANUFACTURING

机译:背景信息计算利用工业互联网(IIOT)创建丰富的数字背景,并将数字线程编织为复合材料制造中的自动化和优化决策

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Composite component fabrication requires real-time decisions based on a long and growing list of variables and constraints. As production volumes and complexity increase, suppliers are faced with more unforeseen problems, and have less time and ability to make optimized decisions. Theory and practice show, that the more variables considered in solving a problem, the better the potential result. However, in attempts to consider the 'big picture', human beings reach their limit at some point, and resort to solving only parts of the challenge, with or without a software designed to address that sub-problem. Currently, integrating this vast and diverse amount of data into a global view of the process is very difficu typically each data set is analyzed in its own software package, limiting the ability to integrate the multiple data types and detect inefficiencies and process issues that might trigger production delays. Integrating Intelligent, context-aware software, based on the Industrial Internet of Things (IIoT) represents dramatic opportunities as new sensor technologies collect vast amounts of data in real time, creating a rich digital context that starts with design engineers and is continuously built through the entire lifecycle of the product. This 'digital thread' weaves a single integrated stream of digital data that makes information from the entire life cycle available and visible to all stakeholders. This would include - among other information - asset location (including pre-preg rolls, kits and assemblies), assets' status and availability (including tools, machines, autoclaves and personnel), exposure time information of parts, kits and assemblies, as well as full genealogical information for each asset or resource, from the moment it was first created, throughout production and beyond into MRO (maintenance, repair, and overhaul). The digital thread model is enabled by the IIoT abilities and supports full traceability of each part, starting at the raw material phase and through its fabrication on the production floor, for later stage auditability and significantly shorter crisis management should a defect occur or be discovered along the way.
机译:复合元件制造需要基于长期和增长的变量和约束列表实时决策。随着产量和复杂性的增加,供应商面临更不可预见的问题,并且具有更少的时间和能力做出优化的决策。理论与实践表明,在解决问题时考虑的变量越多,潜在的结果越好。然而,在尝试考虑“大图”时,人类在某些时候达到了极限,并诉诸只解决了一些挑战的部分,有或没有旨在解决该子问题的软件。目前,将这种巨大和多样化的数据集成到过程的全球视图中非常困难;通常,在其自己的软件包中分析了每个数据集,限制了集成多个数据类型的能力,并检测可能触发生产延迟的效率低下和处理问题。基于工业的事物(IIOT)集成智能,上下文感知软件(IIOT)代表着戏剧性的机会,因为新的传感器技术实时收集大量数据,从而创造了具有设计工程师的丰富的数字上下文,不断地构建产品的整个生命周期。这个“数字线程”编织了单一集成的数字数据流,该数据从所有利益相关者提供的整个生命周期中的信息。这将包括 - 以及其他信息 - 资产位置(包括Pre-Preg卷,套件和组件),资产的状态和可用性(包括工具,机器,高压灭菌器和人员),露天时间信息,以及零件,套件和组件的曝光时间信息作为每个资产或资源的全系谱信息,从首次创建,在整个生产和超越MRO中(维护,修理和大修)。数字线程模型通过IIOT能力实现,并支持每个部分的完全可追溯性,从原料阶段开始,通过其在生产地板上的制造,如果发生缺陷或被发现,危机管理应该明显较短的危机管理道路。

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