Ontology-Based Modelling of State Machines for Production Robots in Smart Manufacturing Systems

2022 ◽  
pp. 429-446
Author(s):  
Alexander Smirnov ◽  
Nikolay Shilov ◽  
Maxim Shchekotov
Keyword(s):  
Manufacturing Systems ◽  
State Machine ◽  
Production Equipment ◽  
Smart Manufacturing ◽  
State Machines ◽  
Promising Direction ◽  
Automated Production ◽  
Processing Power ◽  
Smart Manufacturing Systems ◽  
Different Sources

The integration of modern IT technologies in production equipment does not only enable them to acquire information from different sources and provide it to others but also to make decisions depending on the situation. Due to the limited processing power of such equipment, usage of state machine to describe and program it is considered a promising direction. However, the necessity of intensive interaction of the equipment units causes problems related to interoperability, which are usually solved with the usage of ontologies. The objective of the presented research is to model state machines of production robots via ontologies. The results are demonstrated on the example of a fragment of an automated production line.

Ontology-Based Modelling of State Machines for Production Robots in Smart Manufacturing Systems

2020 ◽  
Vol 11 (2) ◽  
pp. 76-91
Author(s):  
Alexander Smirnov ◽  
Nikolay Shilov ◽  
Maxim Shchekotov
Keyword(s):  
Production Line ◽  
Manufacturing Systems ◽  
Production Equipment ◽  
Smart Manufacturing ◽  
State Machines ◽  
Promising Direction ◽  
Automated Production ◽  
Processing Power ◽  
Smart Manufacturing Systems ◽  
Different Sources

The integration of modern IT technologies in production equipment does not only enable them to acquire information from different sources and provide it to others but also to make decisions depending on the situation. Due to the limited processing power of such equipment, usage of state machine to describe and program it is considered a promising direction. However, the necessity of intensive interaction of the equipment units causes problems related to interoperability, which are usually solved with the usage of ontologies. The objective of the presented research is to model state machines of production robots via ontologies. The results are demonstrated on the example of a fragment of an automated production line.

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.

Are smart manufacturing systems beneficial for all SMEs? Evidence from Korea

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soojeen Jang ◽  
Yanghon Chung ◽  
Hosung Son
Keyword(s):  
Human Resources ◽  
Financial Performance ◽  
Manufacturing Systems ◽  
Contingency Theory ◽  
Ordinary Least Squares ◽  
Operational Efficiency ◽  
Smart Manufacturing ◽  
Least Squares Regression ◽  
Content Type ◽  
Smart Manufacturing Systems

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

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