Exploring the Means and Benefits of Including Blockchain Smart Contracts to a Smart Manufacturing Environment: Water Bottling Plant Case Study

2021 ◽  
pp. 309-319
Author(s):  
O. L. Mokalusi ◽  
R. B. Kuriakose ◽  
H. J. Vermaak
Keyword(s):  
Smart Manufacturing ◽  
Smart Contracts ◽  
Manufacturing Environment

scholarly journals Toward Knowledge Management for Smart Manufacturing

2017 ◽  
Vol 17 (3) ◽  
Author(s):  
Shaw C. Feng ◽  
William Z. Bernstein ◽  
Thomas Hedberg ◽  
Allison Barnard Feeney
Keyword(s):  
Knowledge Management ◽  
Design Process ◽  
Process Planning ◽  
Smart Manufacturing ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Knowledge Objects ◽  
Planning Production

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing (SM). Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of SM. The case study in this paper provides some example knowledge objects to enable SM.

Towards Chinese smart manufacturing ecosystem in the context of the one belt one road initiative

2018 ◽  
Vol 11 (3) ◽  
pp. 291-310 ◽  
Author(s):  
Rafif Al-Sayed ◽  
Jianhua Yang
Keyword(s):  
Industrial Revolution ◽  
Structured Interview ◽  
Industrial Clusters ◽  
Added Value ◽  
Smart Manufacturing ◽  
Content Type ◽  
Factors Affecting ◽  
Industrial Sectors ◽  
Regional Collaboration

Purpose The purpose of this paper is to examine empirically China’s determined thrust to attain a high level of technological innovation and the factors affecting moving towards a smart and sophisticated manufacturing ecosystem in conjunction with the Belt and Road Initiative (OBOR). Design/methodology/approach This research provides empirical determination of the factors affecting moving towards smart manufacturing ecosystems in China. The method is based on combining two approaches: semi-structured interview and questionnaire-based with academics, experts and managers in various Chinese industrial sectors. The results are based on the multivariate analysis of the collected data. A case study of the current manufacturing ecosystem was also analyzed, in order to understand the present state as well as the potential for China’s competitive edge in the developed OBOR countries. Findings The results illustrate the importance of the infrastructure dimension comprising variables related to ecosystems, industrial clusters and Internet of Things IoT and other advanced technologies. A case study of the city of Shenzhen’s transformation into a smart cluster for innovative manufacturing points out how China’s OBOR initiative for regional collaboration will further transform the regional smart clusters into an ultra-large innovation based smart ecosystem. Originality/value This research is the first to study China’ policies towards playing a prominent role in the Fourth Industrial Revolution 4IR in the context of the OBOR initiative, through empirically defining the factors affecting moving towards a knowledge-intensive smart manufacturing ecosystem where the added value is mostly innovation based.

scholarly journals Middleware Application, Suitable to Build an Automated and Connected Smart Manufacturing Environment

2021 ◽  
Author(s):  
Muzaffar Rao ◽  
Thomas Newe
Keyword(s):  
Mobile Robots ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Manufacturing Execution System ◽  
Manufacturing Environment ◽  
Industrial Internet ◽  
A Cell ◽  
Smart Industry ◽  
Manufacturing Execution

The current manufacturing transformation is represented by using different terms like; Industry 4.0, smart manufacturing, Industrial Internet of Things (IIoTs), and the Model-Based enterprise. This transformation involves integrated and collaborative manufacturing systems. These manufacturing systems should meet the demands changing in real-time in the smart factory environment. Here, this manufacturing transformation is represented by the term ‘Smart Manufacturing’. Smart manufacturing can optimize the manufacturing process using different technologies like IoT, Analytics, Manufacturing Intelligence, Cloud, Supplier Platforms, and Manufacturing Execution System (MES). In the cell-based manufacturing environment of the smart industry, the best way to transfer the goods between cells is through automation (mobile robots). That is why automation is the core of the smart industry i.e. industry 4.0. In a smart industrial environment, mobile-robots can safely operate with repeatability; also can take decisions based on detailed production sequences defined by Manufacturing Execution System (MES). This work focuses on the development of a middleware application using LabVIEW for mobile-robots, in a cell-based manufacturing environment. This application works as middleware to connect mobile robots with the MES system.

Shop floor management system in the context of smart manufacturing: a case study

2019 ◽  
Vol 11 (5) ◽  
pp. 837-862 ◽  
Author(s):  
Diamantino Torres ◽  
Carina Pimentel ◽  
Susana Duarte
Keyword(s):  
Cyber Security ◽  
Performance Indicator ◽  
Theoretical Background ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Security Technology ◽  
Content Type ◽  
Smart Technologies ◽  
Manufacturing Technologies

Purpose The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital shop floor (DSF) features. Design/methodology/approach To attain the paper objective, a mixed method methodology was used. In the first stage, a theoretical background was carried out, to provide a comprehensive understanding on SFM system in a smart manufacturing perspective. Next, a case study within a survey was developed. The case study was introduced to characterize a SFM system, while the survey was made to understand the level of influence of smart manufacturing technologies and of DSF features on SFM. In total, 17 experts responded to the survey. Findings Data analytics is the smart manufacturing technology that influences more the SFM system and its components and the cyber security technology does not influence it at all. The problem solving (PS) is the SFM component more influenced by the smart manufacturing technologies. Also, the use of real-time digital visualization tools is considered the most influential DSF feature for the SFM components and the data security protocols is the least influential one. The four SFM components more influenced by the DSF features are key performance indicator tracking, PS, work standardization and continuous improvement. Research limitations/implications The study was applied in one multinational company from the automotive sector. Originality/value To the best of the authors’ knowledge, this work is one of the first to try to characterize the SFM system on smart manufacturing considering smart technologies and DSF features.

scholarly journals A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2815
Author(s):  
Xiaohui Zhang ◽  
Shufeng Tang ◽  
Xinhua Liu ◽  
Reza Malekian ◽  
Zhixiong Li
Keyword(s):  
Virtual Manufacturing ◽  
Edge Computing ◽  
Manufacturing Environment ◽  
Computing Technique ◽  
Agent Based ◽  
System A ◽  
Multi Agent ◽  
Save Energy ◽  
Virtual Manufacturing Environment

This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system.

scholarly journals Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3123 ◽  
Author(s):  
Jonghyuk Kim ◽  
Hyunwoo Hwangbo
Keyword(s):  
Real Time ◽  
Chemical Properties ◽  
Innovation Process ◽  
Smart Manufacturing ◽  
Paradigm Shifts ◽  
Manufacturing Environment ◽  
Pattern Clustering ◽  
Sensing Technology ◽  
Input Variables ◽  
Manual Testing

Recent paradigm shifts in manufacturing have resulted from the need for a smart manufacturing environment. In this study, we developed a model to detect anomalous signs in advance and embedded it in an existing programmable logic controller system. For this, we investigated the innovation process for smart manufacturing in the domain of synthetic rubber and its vulcanization process, as well as a real-time sensing technology. The results indicate that only analysis of the pattern of input variables can lead to significant results without the generation of target variables through manual testing of chemical properties. We have also made a practical contribution to the realization of a smart manufacturing environment by building cloud-based infrastructure and models for the pre-detection of defects.

NAO Robots Collaboration for Object Manipulation

2013 ◽  
Vol 332 ◽  
pp. 218-223 ◽  
Author(s):  
Alina Ninett Panfir ◽  
Răzvan Boboc ◽  
Gheorghe Leonte Mogan
Keyword(s):  
Object Manipulation ◽  
Humanoid Robots ◽  
New Method ◽  
Successful Implementation ◽  
Manufacturing Environment ◽  
Complex Operation ◽  
And Performance ◽  
The Impact ◽  
Specific Scenario

This paper proposes a new method of collaboration within a team of twoindividual NAO robots that should execute together a complex operation. The Naorobots are developed so as not only to act individually, but also to cooperatewith other robots if they cannot accomplish the operation alone. This paperpresents a case study demonstrating the integration of the humanoid roboticsplatform Nao within a cooperation application. This specific scenario ofinterest takes place in a small simulated manufacturing environment; while thetask being the storage of a big object, with different shape and weight. Thisscenario is used to observe the impact and performance that this particularteam of humanoid robots has in an industrial environment.Finally we present the successful implementation of robot – robot cooperationcapabilities inspired by human behaviour.

scholarly journals Are Smart Contracts and Blockchains Suitable for Decentralized Railway Control?

Ledger ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
Michael Kuperberg ◽  
Daniel Kindler ◽  
Sabina Jeschke
Keyword(s):  
Life Cycle Approach ◽  
Central Control ◽  
Smart Contracts ◽  
System State ◽  
Specialized Software ◽  
Blockchain Technology ◽  
Railway Operations ◽  
Software And Hardware ◽  
Condition Based Monitoring

Conventional railway operations employ specialized software and hardware to ensure safe and secure train operations. Track occupation and signaling are governed by central control offices, while trains (and their drivers) receive instructions. To make this setup more dynamic, the train operations can be decentralized by enabling the trains to find routes and make decisions which are safeguarded and protocolled in an auditable manner. In this paper, we present the case study findings of a first-of-its-kind blockchain-based prototype implementation for railway control, based on decentralization but also ensuring that the overall system state remains conflict-free and safe. We also show how a blockchain-based approach simplifies usage billing and enables a train-to-train/machine-to-machine economy. Finally, first ideas addressing the use of blockchain technology as a life-cycle approach for condition-based monitoring and predictive maintenance in train operations are outlined.

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