scholarly journals Real-Time Advanced Analytics, Automated Production Systems, and Smart Industrial Value Creation in Sustainable Manufacturing Internet of Things

2021 ◽  
Vol 9 (2) ◽  
pp. 32
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Value Creation ◽  
Production Systems ◽  
Sustainable Manufacturing ◽  
Automated Production ◽  
Advanced Analytics

An agent-based approach for dependable planning of production sequences in automated production systems

2017 ◽  
Vol 65 (11) ◽  
Author(s):  
Sebastian Rehberger ◽  
Lucas Spreiter ◽  
Birgit Vogel-Heuser
Keyword(s):  
Software Engineering ◽  
Real Time ◽  
Production Systems ◽  
Knowledge Bases ◽  
Design Parameters ◽  
Product Management ◽  
Plant Resources ◽  
Time Duration ◽  
Automated Production ◽  
Agent Oriented Software Engineering

AbstractOne approach to achieve flexibility and dependability for the control of automated production systems (aPS) is agent-oriented software engineering (AOSE). In this paper, the modular decoupling of the supervisory control, most significantly the planning of production sequences and transfer routes, from the distributed real-time control of the plant resources is demonstrated by the use of agents. The resulting product management agent (PMA) represents the technical process of the manufactured product and conducts a discrete reasoning algorithm to derive appropriate production plans by the use of graph-search and also by interaction with the underlying resource agents (RA). It is shown, that for a given production system, dependable solutions are automatically generated in regard to a given product recipe. Further it is deduced, that the solutions are calculated and evaluated by the PMA within a deterministic time duration. This is argued on the fact, that the computation complexity does not exceed polynomial time and is mostly predetermined by the design parameters of the plant. Thus, it gives a reasonable approach for the use in a real-time environment. Additionally, through separation of supervisory and field control, a modular software engineering is achieved, offering the advantage that the PMA and the resource agents can be reused, by solely adapting the knowledge bases and without the need for modifying the planning algorithms after a reconfiguration of the aPS.

scholarly journals Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2497
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Cristian Uță ◽  
Roxana Ștefănescu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Manufacturing Systems ◽  
Process Management ◽  
Production Systems ◽  
Big Data Analytics ◽  
Eligibility Criteria ◽  
Cyber Physical Production Systems

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

scholarly journals CONCEPTUAL FRAMEWORK FOR APPLYING INTERNET OF THINGS IN PRODUCTION SYSTEMS FOR SENSING ENTERPRISES

2016 ◽  
Vol 13 (1) ◽  
pp. 66
Author(s):  
Andrés Boza ◽  
Beatriz Cortes ◽  
Maria del Mar Eva Alemany ◽  
Llanos Cuenca
Keyword(s):  
Internet Of Things ◽  
Literature Review ◽  
Conceptual Framework ◽  
Real Time ◽  
Production Systems ◽  
The Internet ◽  
Main Components ◽  
Real Time Information ◽  
Time Information ◽  
The Internet Of Things

Sensing Enterprise is a new concept, which appears with the Internet of Things (IoT) application in industry. This technology applied in production sys- tems provides many benefits like better transparency or real time information. This approach proposes a conceptual framework for IoT application in Production Systems. The aim of this framework is helping enterprises to identify the main el- ements to apply IoT in Production Systems. To create this framework, a literature review has been made and the main components of IoT in Sensing Enterprise in production proposals have been identify. Thus, these elements and its relations have been the source for the conceptual framework proposed. 

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