scholarly journals Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

2019 ◽  
Author(s):  
Alireza Zarreh ◽  
HungDa Wan ◽  
Yooneun Lee ◽  
Can Saygin ◽  
Rafid Al Janahi
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Performance Indicator ◽  
Negative Impact ◽  
Smart Manufacturing ◽  
Network Systems ◽  
Total Productive Maintenance ◽  
Core Function ◽  
Traditional Manufacturing ◽  
Smart Manufacturing Systems

Maintenance is the core function to keep a system running and avoid failure. Total Productive Maintenance (TPM) has broadly utilized maintenance strategy to improve the customer's satisfaction and hence obtain a competitive advancement. However, the complexity of smart manufacturing systems due to the recent advancements, specifically the integration of internet and network systems with traditional manufacturing platforms, has made this function more challenging. The focus of this paper is to explain how cybersecurity could impact the TPM by affecting the overall equipment effectiveness (OEE) in a smart manufacturing system by providing a structured literature survey. First, it provides concerns on principle of TPM regarding cybersecurity in smart manufacturing systems. Then, it highlights the effect of a variety of cyber-physical threats on OEE, as a main key performance indicator of TPM and how differently they can reduce OEE. The countermeasures that could be considered to compensate for the negative impact of a cybersecurity threat on the overall effectiveness of the system also will be discussed. Finally, research gaps and challenges are identified to improve overall equipment effectiveness (OEE) in presence of cybersecurity threats in critical manufacturing industries.

An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering

2018 ◽  
Vol 233 (11) ◽  
pp. 2210-2224 ◽  
Author(s):  
Yuanju Qu ◽  
Xinguo Ming ◽  
Yanrong Ni ◽  
Xiuzhen Li ◽  
Zhiwen Liu ◽  
...  
Keyword(s):  
Information Systems ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Process Reengineering ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Traditional Manufacturing ◽  
Systems Framework ◽  
Smart Manufacturing Systems

Enterprise information systems play a significant role in the Industry 4.0 era and are the crucial component to realize smart manufacturing systems. However, traditional enterprise information systems have some limits: (1) lack of complete information, (2) only satisfy limited business needs, and (3) lack of seamless integration, business intelligence, value-driven processes, and dynamic optimization. Clearly, the existing enterprise information systems are unable to satisfy the requirements for smart manufacturing systems: (1) autonomous operation, (2) sustainable values, and (3) self-optimization. In addition, smart manufacturing systems have become more efficient and effective, demanding for seamless information flow in enterprise information systems, knowledge, and data-driven accurately decision. Therefore, a new enterprise information systems framework is needed to bridge gaps between the requirements for traditional manufacturing system and smart manufacturing system. In this article, the integrative framework is proposed based on the business process reengineering, lean thinking, and intelligent management methods, with inclusion of six enterprise information systems aspects to provide upgrading guidelines from traditional manufacturing to smart manufacturing. The procedure of this method contains three steps: (1) it identifies requirements and acquires best practices using AS-IS model, (2) it redesigns six aspects of enterprise information systems using TO-BE model, and (3) it proposes a new enterprise information systems framework. Finally, the proposed framework is validated by real cases.

scholarly journals Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

2019 ◽  
Vol 38 ◽  
pp. 532-539 ◽  
Author(s):  
Alireza Zarreh ◽  
HungDa Wan ◽  
Yooneun Lee ◽  
Can Saygin ◽  
Rafid Al Janahi
Keyword(s):  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Total Productive Maintenance ◽  
Smart Manufacturing Systems

scholarly journals Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Benjamin Y. Choo ◽  
Stephen C. Adams ◽  
Brian A. Weiss ◽  
Jeremy A. Marvel ◽  
Peter A. Beling
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Health Management ◽  
Smart Manufacturing ◽  
Health State ◽  
Prognostics And Health Management ◽  
Multi Scale ◽  
Decision Points ◽  
The Hierarchical Structure ◽  
Smart Manufacturing Systems

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decisionmaking in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.

Sustaining the Competitive Edge of Small to Medium Size Enterprises Through Innovative Manufacturing System Design

2014 ◽  
Author(s):  
Mohamed A. Gadalla
Keyword(s):  
System Design ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Medium Size ◽  
Market Conditions ◽  
Competitive Edge ◽  
Manufacturing System Design ◽  
Smart Manufacturing Systems

Increasing Small to Medium size Enterprises (SME’s) competitive edge requires continuously developing creative and novel methods and solutions. This paper presents a novel design for a manufacturing system named Smart Manufacturing Systems (SMS). The new design can be viewed as a modification to the Flexible Manufacturing System (FMS) to better suits continuously changing market conditions, which may lead a company to develop a more sustainable competitive edge. The new design address several issues in manufacturing system design that affect the competitiveness of the system such as: merger of different manufacturing processes, non-productive times, and to be able to performing economically under different market conditions.

scholarly journals Generic Design Methodology for Smart Manufacturing Systems from a Practical Perspective. Part II—Systematic Designs of Smart Manufacturing Systems

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 208
Author(s):  
Zhuming Bi ◽  
Wen-Jun Zhang ◽  
Chong Wu ◽  
Chaomin Luo ◽  
Lida Xu
Keyword(s):  
Design Methodology ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Performance Metrics ◽  
Reference Model ◽  
Smart Manufacturing ◽  
Axiomatic Design Theory ◽  
Manufacturing Resources ◽  
Generic Design ◽  
Smart Manufacturing Systems

In a traditional system paradigm, an enterprise reference model provides the guide for practitioners to select manufacturing elements, configure elements into a manufacturing system, and model system options for evaluation and comparison of system solutions against given performance metrics. However, a smart manufacturing system aims to reconfigure different systems in achieving high-level smartness in its system lifecycle; moreover, each smart system is customized in terms of the constraints of manufacturing resources and the prioritized performance metrics to achieve system smartness. Few works were found on the development of systematic methodologies for the design of smart manufacturing systems. The novel contributions of the presented work are at two aspects: (1) unified definitions of digital functional elements and manufacturing systems have been proposed; they are generalized to have all digitized characteristics and they are customizable to any manufacturing system with specified manufacturing resources and goals of smartness and (2) a systematic design methodology has been proposed; it can serve as the guide for designs of smart manufacturing systems in specified applications. The presented work consists of two separated parts. In the first part of paper, a simplified definition of smart manufacturing (SM) is proposed to unify the diversified expectations and a newly developed concept digital triad (DT-II) is adopted to define a generic reference model to represent essential features of smart manufacturing systems. In the second part of the paper, the axiomatic design theory (ADT) is adopted and expanded as the generic design methodology for design, analysis, and assessment of smart manufacturing systems. Three case studies are reviewed to illustrate the applications of the proposed methodology, and the future research directions towards smart manufacturing are discussed as a summary in the second part.

scholarly journals Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 92
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Learning Approaches ◽  
Blockchain Technology ◽  
Timely Response ◽  
Machine Learning Approach ◽  
Smart Manufacturing Systems

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.

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

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.

scholarly journals A Model for Working Environment Monitoring in Smart Manufacturing

2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  
Keyword(s):  
Open Source ◽  
Student Teaching ◽  
Manufacturing Systems ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Environment Monitoring ◽  
Application Development ◽  
Testing Platform ◽  
Proposed Model ◽  
Smart Manufacturing Systems

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

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