机器学习与工业4.0的共生关系综述

M. Azeem, Abid Haleem, M. Javaid
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引用次数: 13

摘要

工业4.0虽然推出不到十年,但已经彻底改变了技术的使用方式。它在制造业、网络安全、健康、银行和其他服务的几乎每个领域都有应用。工业4.0在很大程度上依赖于互联和数据。机器学习(ML)是构建工业4.0应用的基础。在本文中,我们提供了一个广泛的视角,说明机器学习是如何实现工业4.0的好处的。本文包括ML在公司中的使用以及ML的局限性,这些局限性需要缓解。还有一些ML算法失败的例子及其后果。虽然工业4.0需要比正常流程更多的投入和资本,但长期收益大于初始成本。机器学习越来越受欢迎,广泛的研究正在开发其潜力并开发完整的智能应用程序。
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Symbiotic Relationship Between Machine Learning and Industry 4.0: A Review
Industry 4.0 though launched less than a decade ago, has revolutionized the way technologies are being used. It has found its application in almost every field of manufacturing, cybersecurity, health, banking, and other services. Industry 4.0 is heavily dependent on interconnectivity and data. Machine learning (ML) acts as a foundation for building industry 4.0 applications. In this paper, we have provided a broad view of how ML is necessary to accomplish the benefits of industry 4.0. The paper includes ML usage in companies and the limitations of ML, which need to be mitigated. There are also some instances of the failure of ML algorithms and their repercussions. Though industry 4.0 requires a lot more inputs and capital than normal processes, the long-run benefits outweigh the initial costs. ML is gaining popularity, and extensive research is happening to exploit its potential and develop full smart applications.
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来源期刊
CiteScore
17.00
自引率
16.70%
发文量
31
期刊介绍: The Journal of Industrial Integration and Management: Innovation & Entrepreneurship concentrates on the technological innovation and entrepreneurship within the ongoing transition toward industrial integration and informatization. This journal strives to offer insights into challenges, issues, and solutions associated with industrial integration and informatization, providing an interdisciplinary platform for researchers, practitioners, and policymakers to engage in discussions from the perspectives of innovation and entrepreneurship. Welcoming contributions, The Journal of Industrial Integration and Management: Innovation & Entrepreneurship seeks papers addressing innovation and entrepreneurship in the context of industrial integration and informatization. The journal embraces empirical research, case study methods, and techniques derived from mathematical sciences, computer science, manufacturing engineering, and industrial integration-centric engineering management.
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