Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges, and Future Direction

被引:0
|
作者
Rakholia, Rajnish [1 ]
Suarez-Cetrulo, Andres L. [1 ]
Singh, Manokamna [1 ]
Simon Carbajo, Ricardo [1 ]
机构
[1] Univ Coll Dublin UCD, Irelands Ctr Artificial Intelligence CeADAR, Sch Comp Sci, Dublin 4, Ireland
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Artificial intelligence; Manufacturing; Industries; Machine learning algorithms; Reviews; Automation; Production; Smart manufacturing; Fourth Industrial Revolution; Quality control; Internet of Things; Best practices; Industry; 4.0; artificial intelligence; automation; machine learning; quality control; robotics; INDUSTRY; 4.0; SMART; CLASSIFICATION; CONTEXT; NETWORK; SYSTEM;
D O I
10.1109/ACCESS.2024.3458830
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The industrial sector is currently undergoing a transformative era of intelligent automation driven by Artificial Intelligence (AI) capabilities. This synergy greatly enhances efficiency and seamlessly enables data-driven decision-making processes. These advantages enable more efficient resource allocation and enhance production planning precision. This paper aims to provide state-of-the-art and ongoing developments in the AI landscape within the manufacturing industry. In addition, the review explores the key areas where AI is being applied in manufacturing, such as predictive maintenance, quality control, process optimization, supply chain management, robotics and automation, and intelligent decision support systems. The review also encompasses an exploration of the challenges encountered by the manufacturing sector, alongside an investigation into the potential of AI to mitigate these challenges. Furthermore, this work thoroughly reviews recent AI advancements, including explainable AI, human-robot collaboration, edge computing, and the Internet of Things (IoT) integration. The review concludes by providing recommendations, highlighting best practices, and providing insights into potential collaborative opportunities.
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页码:131621 / 131637
页数:17
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