Cyber-Physical Smart Manufacturing Systems: Sustainable Industrial Networks, Cognitive Automation, and Big Data-driven Innovation

Manufacturers have faced an increasing need for the development of predictive models that help predict mechanical failures and remaining useful life of a manufacturing system or its system components. Model-based or physics-based prognostics develops mathematical models based on physical laws or probability distributions, while an in-depth physical understanding of system behaviors is required. In practice, however, some of the distributional assumptions do not hold true. To overcome the limitations of model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While earlier work demonstrated the effectiveness of data-driven approaches, most of these methods applied to prognostics and health management (PHM) in manufacturing are based on artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to explore the ability of random forests (RFs) to predict tool wear in milling operations. The performance of ANNs, SVR, and RFs are compared using an experimental dataset. The experimental results have shown that RFs can generate more accurate predictions than ANNs and SVR in this experiment.

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Cognitive Automation, Big Data-driven Manufacturing, and Sustainable Industrial Value Creation in Internet of Things-based Real-Time Production Logistics

Economics Management and Financial Markets ◽

10.22381/emfm15420204 ◽

2020 ◽

Vol 15 (4) ◽

pp. 39

Keyword(s):

Big Data ◽

Internet Of Things ◽

Real Time ◽

Value Creation ◽

Data Driven ◽

Production Logistics ◽

Time Production ◽

Cognitive Automation

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Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

Sustainability ◽

10.3390/su13105495 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5495

Author(s):

Mihai Andronie ◽

George Lăzăroiu ◽

Roxana Ștefănescu ◽

Cristian Uță ◽

Irina Dijmărescu

Keyword(s):

Decision Making ◽

Internet Of Things ◽

Literature Review ◽

Manufacturing Systems ◽

Manufacturing System ◽

Data Driven ◽

Smart Manufacturing ◽

Production Networks ◽

Production Logistics ◽

Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

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