通过对比文本挖掘探索物联网在精密加工中的应用

Edward Hengzhou Yan, Feng Guo, Baolong Zhang, Muhammad Rehan, Delei Wang, Zhicheng Xu, Chi Ho Wong, Long Teng, Wai Sze Yip, Suet To
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引用次数: 0

摘要

精密加工,制造具有卓越表面质量和尺寸精度的部件,越来越多地利用物联网(IoT)技术。本研究采用了一种新颖的比较文本挖掘方法,通过系统地集成树图、词云、关键词网络分析和Pearson相关性来识别物联网与精密加工之间的关键联系。通过对一个科研数据库(2019-2023)的分析,本研究强调了物联网在提高精密加工方面的核心竞争力,包括实时监控、预测性维护和数据驱动优化。此外,本研究提出了可操作的策略,包括基于神经网络的网络生产系统、区块链集成工业物联网平台和机器学习驱动的精密加工预测模型。这些建议使学术界和工业界能够利用物联网来提高产品质量并降低精密加工的成本。本文分为:算法开发;文本挖掘数据和知识的基本概念;知识表示技术;数据预处理
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Application of the Internet of Things in Precision Machining by Comparative Text Mining
Precision machining, manufacturing components with superior surface quality and dimensional accuracy, increasingly leverages Internet of Things (IoT) technologies. This study employs a novel comparative text mining approach by systematically integrating tree maps, word clouds, keyword network analysis, and Pearson correlation to identify critical linkages between IoT and precision machining. By analyzing a scientific research database (2019–2023), this study highlights IoT's core competencies in enhancing precision machining, including real‐time monitoring, predictive maintenance, and data‐driven optimization. Furthermore, this study proposes actionable strategies, including neural network‐based cyber production systems, blockchain‐integrated IIoT platforms, and machine learning‐driven predictive models, for precision machining. These recommendations empower academia and industry to harness IoT to improve product quality and reduce costs in precision machining.This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Data Preprocessing
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