深度学习在制造业应用中的作用:挑战与机遇

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computing and Information Science in Engineering Pub Date : 2023-07-11 DOI:10.1115/1.4062939
R. Malhan, S. Gupta
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引用次数: 1

摘要

人们对在制造业中使用深度学习技术来提高质量、生产率、安全性和效率越来越感兴趣,同时也降低了成本和周期时间。本文讨论了目前正在使用的深度学习的主要应用,包括在高混合生产中识别缺陷、优化流程、简化供应链、预测维护需求和识别人类活动。本文简要总结了深度学习技术的各个组成部分及其作用。此外,本文提请注意当前需要解决的挑战和限制,以充分发挥深度学习技术在制造业中的潜力。最后,提出了该领域未来的几个研究方向,以进一步提高深度学习在制造业中的应用。
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The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities
There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This paper discusses the primary applications of deep learning currently being employed, including identifying defects during high-mix production, optimizing processes, streamlining the supply chain, predicting maintenance needs, and recognizing human activity. The paper offers a brief summary of the various components of deep learning technology and their roles. Additionally, the paper draws attention to the current challenges and limitations that need to be addressed to fully realize the potential of deep learning technology in manufacturing. Lastly, several future directions for research within the field are proposed to further improve the use of deep learning in manufacturing.
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来源期刊
CiteScore
6.30
自引率
12.90%
发文量
100
审稿时长
6 months
期刊介绍: The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping
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