Intelligent Manufacturing in the Context of Industry 4.0: A Review

By Ray Y. Zhong, Xun Xu, E. Klotz, Stephen T. Newman. 2017

2,151 citations recorded by the source corpus.

Abstract Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

Subfields: Industrial and Manufacturing Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “, haptic), going beyond classification tasks [63], necessitating both eMBB and URLLC.”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, background): “Technology has provided companies with more eﬃcient and reliable monitoring systems [11].”
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “[7,8], a high-level, general framework and key elements in smart manufacturing systems and governmental initiatives around the globe are presented.”
Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies (2019, background): “In a smart manufacturing system, the manufacturing processes are more flexible, intelligent, and agile, and are well equipped to meet the challenges of a dynamic and global market (Shen and Norrie 1999; Zhong et al. 2017).”
Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing (2019, background): “…Industrie 4.0 in Germany, Made in China 2025, and Advanced Manufacturing Partnership in United States, among which intelligent manufacturing is regarded as the key to establish the competitive advantages for the manufacturing industry of major countries (Zhou et al. 2018; Zhong et al. 2017).”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “In determining the major concepts and key methodologies, the authors in [44] reviewed intelligent manufacturing systems.”
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, background): “Manufacturing is shifting from knowledge-based intelligent manufacturing to data-drivenandknowledge-enabledsmartmanufacturing,inwhich theterm‘‘smart”referstothecreationanduseofdata[4].”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “AI centric technologies will be able to monitor and control processes in real time o ﬀ ering signi ﬁ cant e ﬃ ciencies over manual processes (Jain & Mosier, 1992; Zhong, Xu, Klotz, & Newman, 2017a).”
Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, background): “The adoption of information and communication technology (ICT) in manufacturing enables a transition from traditional to advanced manufacturing processes [5].”