Object-oriented event-graph modeling formalism to simulate manufacturing systems in the Industry 4.0 era

2020 ◽  
Vol 99 ◽  
pp. 102027 ◽  
Author(s):  
Lorenzo Tiacci
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Object Oriented ◽  
Graph Modeling ◽  
Modeling Formalism

scholarly journals Open Source Control Device for Industry 4.0 Based on RAMI 4.0

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 869
Author(s):  
Pablo F. S. Melo ◽  
Eduardo P. Godoy ◽  
Paolo Ferrari ◽  
Emiliano Sisinni
Keyword(s):  
Open Source ◽  
Industry 4.0 ◽  
Product Life Cycle ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Control Device ◽  
Source Control ◽  
Reference Architecture ◽  
Architecture Model ◽  
Complex Relationships

The technical innovation of the fourth industrial revolution (Industry 4.0—I4.0) is based on the following respective conditions: horizontal and vertical integration of manufacturing systems, decentralization of computing resources and continuous digital engineering throughout the product life cycle. The reference architecture model for Industry 4.0 (RAMI 4.0) is a common model for systematizing, structuring and mapping the complex relationships and functionalities required in I4.0 applications. Despite its adoption in I4.0 projects, RAMI 4.0 is an abstract model, not an implementation guide, which hinders its current adoption and full deployment. As a result, many papers have recently studied the interactions required among the elements distributed along the three axes of RAMI 4.0 to develop a solution compatible with the model. This paper investigates RAMI 4.0 and describes our proposal for the development of an open-source control device for I4.0 applications. The control device is one of the elements in the hierarchy-level axis of RAMI 4.0. Its main contribution is the integration of open-source solutions of hardware, software, communication and programming, covering the relationships among three layers of RAMI 4.0 (assets, integration and communication). The implementation of a proof of concept of the control device is discussed. Experiments in an I4.0 scenario were used to validate the operation of the control device and demonstrated its effectiveness and robustness without interruption, failure or communication problems during the experiments.

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

Modulare Fertigungslinien für die individualisierte Produktion/Modular manufacturing systems for individualized production - Improving flexibility through business process modelling

2016 ◽  
Vol 106 (04) ◽  
pp. 204-210
Author(s):  
S. Wrede ◽  
M. Wojtynek ◽  
J. Prof. Steil ◽  
O. Beyer ◽  
C. Frobieter ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Leading Edge ◽  
Industrie 4.0 ◽  
Process Models ◽  
Modular System ◽  
Lot Size ◽  
Business Process Modelling ◽  
Individualized Production ◽  
Modular Production

Der Beitrag beschreibt ein Hard- und Softwarekonzept für vernetzte Fertigungsmodule. Eine modulare Systemarchitektur sowie die dezentrale Steuerung durch Prozessmodelle auf Basis von BPMN2 erlauben eine kundenspezifische Produktion bis hin zu Losgröße eins. Anhand eines vertikal in die Unternehmens-IT integrierten Demonstrators wurden die Vorteile als Showcase für Industrie 4.0 auf verschiedenen Fachmessen erlebbar. Der innovative Ansatz wurde im Verbundprojekt itsowl-FlexiMon im Rahmen des BMBF Spitzenclusters „Intelligente Technische Systeme OstWestfalenLippe“ (it’s OWL) entwickelt.   This contribution describes a distributed modular production system for individualized production. A modular system architecture and semi-autonomous cell control based on executable process models with BPMN2 are used to realize a customer specific production down to lot size one. The advantages have become tangible through a vertically integrated demonstrator that has been exhibited at different fares and showcases the progress towards Industry 4.0. The overall approach was developed in the project itsowl-FlexiMon in the framework of the BMBF leading edge cluster „Intelligent Technical Systems OWL“ (it’s OWL).

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.

Espousal of Industry 4.0 in Indian Manufacturing Organizations

2020 ◽  
pp. 245-252
Author(s):  
Ravinder Kumar
Keyword(s):  
Power Generation ◽  
Decision Making ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Assembly Lines ◽  
Steam Power ◽  
The Third ◽  
Manufacturing Sectors ◽  
Manufacturing Organizations ◽  
Modern Era

A formal industrialization commenced with steam power generation and the application of machines that mechanized the industrial work in past. Subsequently, the development in electric power, the assembly lines, and mass manufacturing led toward the third era of numeric control and automation. Now in modern era of industry 4.0, robots connected with the computers and machines. Tools are working on machines learning algorithms and running the cyber physical manufacturing systems. Sensing the need of hour, Indian manufacturing organizations are working hard to implement the practices of Industry 4.0. Working on identical direction, the author has identified 12 enablers poignant the espousal of Industry 4.0 in Indian manufacturing sectors from literature review and by opinion of experts. Further, the author has used Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for developing the structural and circumstantial kinship among the enablers of Industry 4.0.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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