scholarly journals Empowering ISA95 compliant traditional and smart manufacturing systems with the blockchain technology

2021 ◽  
Vol 8 ◽  
pp. 15
Author(s):  
Erkan Yalcinkaya ◽  
Antonio Maffei ◽  
Hakan Akillioglu ◽  
Mauro Onori
Keyword(s):  
Data Privacy ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
M2m Communication ◽  
Industrial Manufacturing ◽  
Blockchain Technology ◽  
Industrial Internet ◽  
Research Gap ◽  
Smart Manufacturing Systems ◽  
Technology Domain

Technological advancements in the information technology domain such as cloud computing, industrial internet of things (IIoT), machine to machine (M2M) communication, artificial intelligence (AI), etc. have started to profoundly impact and challenge not only the ISA95 compliant traditional (ISA95-CTS) but also the smart manufacturing systems (SMMS). Our literature survey pinpoints that systems scalability, interoperability, information security, and data quality domains are among those where many challenges occur. Blockchain technology (BCT) is a new breed of technology characterized by decentralized verifiability, transparency, data privacy, integrity, high availability, and data protection properties. Although many researchers leveraged BCT to empower various aspects of industrial manufacturing systems, there is no study dedicated to addressing the challenges impacting the manufacturing systems compliant with the ISA95 standard. Thereby, our study aims to fill the identified research gap systematically. This paper thoroughly analyzes the challenges hampering the ISA95-CTS and SMMS and methodically addresses them with corresponding BCT capabilities. Furthermore, this paper also discusses various aspects, including the weaknesses, of BCT convergence to ISA95-CTS and SMMS.

scholarly journals Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6456 ◽  
Author(s):  
Erkan Yalcinkaya ◽  
Antonio Maffei ◽  
Mauro Onori
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Research Effort ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Systematic Assessment ◽  
Blockchain Technology ◽  
Design Requirements ◽  
Manufacturing Technologies ◽  
Smart Manufacturing Systems

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.

scholarly journals BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

2019 ◽  
Vol 9 (1S3) ◽  
pp. 366-378
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Real Life ◽  
Cloud Services ◽  
Smart Manufacturing ◽  
Small Scale ◽  
Automotive Manufacturing ◽  
Blockchain Technology ◽  
Decentralized Decision Making ◽  
Smart Manufacturing Systems

Blockchain is going to be the most fundamental technology, and will change the world — going forward. In fact, the revolution has already begun. The birth of Industry 4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure manufacturing systems. The current smart systems lack the decentralized decision making and real-time communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers and other peers. Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards Industry 4.0.

A GEMMA-GRAFCET Generator for the Automation Software of Smart Manufacturing Systems

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 232
Author(s):  
Juan Manuel Castillo ◽  
Giacomo Barbieri ◽  
Alejandro Mejia ◽  
José Daniel Hernandez ◽  
Kelly Garces
Keyword(s):  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Online Tool ◽  
Model Driven ◽  
Manufacturing Enterprises ◽  
Virtual Commissioning ◽  
Industrial Internet ◽  
Smart Manufacturing Systems ◽  
Operational Modes

Within the Industry 4.0 revolution, manufacturing enterprises are transforming to intelligent enterprises constituted by Smart Manufacturing Systems (SMSs). A key capability of SMSs is the ability to connect and communicate with each other through Industrial Internet of Things technologies, and protocols with standard syntax and semantics. In this context, the GEMMA-GRAFCET Methodology (GG-Methodology) provides a standard approach and vocabulary for the management of the Operational Modes (OMs) of SMSs through the automation software, bringing a common understanding of the exchanged data. Considering the lack of tools to implement the methodology, this work introduces an online tool based on Model-Driven Engineering–GEMMA-GRAFCET Generator (GG-Generator)–to specify and generate PLCopen XML code compliant with the GG-Methodology. The proposed GG-Generator is applied to a case study and validated using Virtual Commissioning and Dynamic Software Testing. Due to the consistency obtained between the GG-Methodology and the generated PLC code, the GG-Generator is expected to support the adoption of the methodology, thus contributing to the interoperability of SMSs through the standardization of the automation software for the management of their OMs.

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 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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