scholarly journals Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance

2020 ◽  
Vol 11 (1) ◽  
pp. 31
Author(s):  
Izabela Rojek ◽  
Dariusz Mikołajewski ◽  
Ewa Dostatni
Keyword(s):  
Process Parameters ◽  
Industry 4.0 ◽  
Simulation Models ◽  
Living Organism ◽  
Sustainable Production ◽  
Physical Models ◽  
Product Lifecycle ◽  
Technical Object ◽  
Production Processes ◽  
Digital Twins

A “digital twin” is a dynamic, digital replica of a technical object (e.g., a physical system, device, machine or production process) or a living organism. Using this type of solution has become an integral part of Industry 4.0, offering businesses tangible benefits, in addition to being particularly effective within the context of sustainable production and maintenance. The purpose of this paper is to present the results of research on the development of digital twins of technical objects, which involved data acquisition and their conversion into knowledge, the use of physical models to simulate tasks and processes, and the use of simulation models to improve the physical tasks and processes. In addition, monitoring processes and process parameters allow for the continued improvement of existing processes as regards intelligent eco-designing and planning and monitoring production processes while taking into account sustainable production and maintenance.

scholarly journals Cognitive Digital Twins for Resilience in Production: A Conceptual Framework

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 33
Author(s):  
Pavlos Eirinakis ◽  
Stavros Lounis ◽  
Stathis Plitsos ◽  
George Arampatzis ◽  
Kostas Kalaboukas ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Industry 4.0 ◽  
Cognitive Process ◽  
Manufacturing Systems ◽  
Real Life ◽  
Production Processes ◽  
Digital Twins ◽  
Similarities And Differences

Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in production, i.e., to enable manufacturing systems to identify and handle anomalies and disruptive events in production processes and to support decisions to alleviate their consequences. Through analyzing five real-life production cases in different industries, similarities and differences in their corresponding needs are identified. Moreover, a connection between resilience and cognition is established. Further, a conceptual architecture is proposed that maps the tools materializing cognition within the DT core together with a cognitive process that enables resilience in production by utilizing CDTs.

scholarly journals Case Study of Building a Digital Twin for a Production System

2021 ◽  
Vol 29 (49) ◽  
pp. 51-58
Author(s):  
Fedor Burčiar ◽  
Pavel Važan ◽  
Simona Pulišová
Keyword(s):  
Production System ◽  
Industry 4.0 ◽  
Production Systems ◽  
Simulation Models ◽  
Cyber Physical Systems ◽  
Production Data ◽  
Physical Systems ◽  
Production Processes ◽  
Gathering Data

Abstract As the term of Industry 4.0 becomes more and more relevant with each passing day, it is up to researchers and companies to find solutions to integrating all the technologies it covers. One of those technologies, even though not highly developed, is simulation and building Cyber-Physical Systems for gathering data and improving the production processes. In the research described in this paper, we focused on integrating production data with simulation models in order to make the process of understanding and learning about complex production systems as simple and as quick as possible. This paper contains three sections. The first one introduces the theoretical fundamentals of our research. The second one focuses on the methods used to create a digital model of production system. The final one discusses the results of the conducted experiments, and their impact on further research.

scholarly journals Concept and Case Study for a Generic Simulation as a Digital Shadow to Be Used for Production Optimisation

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1362
Author(s):  
Stefan Kassen ◽  
Holger Tammen ◽  
Maximilian Zarte ◽  
Agnes Pechmann
Keyword(s):  
Enterprise Resource Planning ◽  
Production Systems ◽  
Simulation Models ◽  
Resource Planning ◽  
Production Plant ◽  
Physical Test ◽  
Production Program ◽  
Production Processes ◽  
Digital Twins

Optimising an existing production plant is a challenging task for companies. Necessary physical test runs disturb running production processes. Simulation models are one opportunity to limit these physical test runs. This is particularly important since today’s fast and intelligent networking opportunities in production systems are in line with the call of Industry 4.0 for substantial and frequent changes. Creating simulation models for those systems requires high effort and in-depth knowledge of production processes. In the current literature, digital twins promise several advantages for production optimisation and can be used to simulate production systems, which reduce necessary physical test runs and related costs. While most companies are not able to create digital twins yet, companies using enterprise resource planning (ERP) systems have the general capability to create digital shadows. This paper presents a concept and a case study for a generic simulation of production systems in AnyLogic™ to create digital shadows as the first step towards a full digital twin. The generic simulation visualises production systems automatically and displays key performance indicators (KPIs) for the planned production program, using representational state transfer (REST) interfaces to extract product and production data from an ERP system. The case study has been applied in a learning factory of the University of Applied Life Sciences Emden/Leer. The results prove the presented concept of the generic simulation and show the limits and challenges of working with generic simulation models.

scholarly journals Process Modeling and Simulation of Tableting—An Agent-Based Simulation Methodology for Direct Compression

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 996
Author(s):  
Niels Lasse Martin ◽  
Ann Kathrin Schomberg ◽  
Jan Henrik Finke ◽  
Tim Gyung-min Abraham ◽  
Arno Kwade ◽  
...  
Keyword(s):  
Raw Materials ◽  
Pilot Scale ◽  
Physical Models ◽  
Agent Based Simulation ◽  
Validation Data ◽  
Agent Based ◽  
Industrial Processing ◽  
Digital Twins ◽  
Product And Process ◽  
The Impact

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

scholarly journals Special Issue of the Manufacturing Engineering Society 2020 (SIMES-2020)

2021 ◽  
Vol 11 (13) ◽  
pp. 5975
Author(s):  
Ana María Camacho ◽  
Eva María Rubio
Keyword(s):  
Applied Sciences ◽  
Modeling And Simulation ◽  
Industry 4.0 ◽  
Product Lifecycle Management ◽  
Special Issue ◽  
Product Lifecycle ◽  
Manufacturing Automation ◽  
Engineering Society ◽  
Manufacturing Engineering ◽  
Lifecycle Management

The Special Issue of the Manufacturing Engineering Society 2020 (SIMES-2020) has been launched as a joint issue of the journals “Materials” and “Applied Sciences”. The 14 contributions published in this Special Issue of Applied Sciences present cutting-edge advances in the field of Manufacturing Engineering focusing on advances and innovations in manufacturing processes; additive manufacturing and 3D printing; manufacturing of new materials; Product Lifecycle Management (PLM) technologies; robotics, mechatronics and manufacturing automation; Industry 4.0; design, modeling and simulation in manufacturing engineering; manufacturing engineering and society; and production planning. Among them, the topic “Manufacturing engineering and society” collected the highest number of contributions (representing 22%), followed by the topics “Product Lifecycle Management (PLM) technologies”, “Industry 4.0”, and “Design, modeling and simulation in manufacturing engineering” (each at 14%). The rest of the topics represent the remaining 35% of the contributions.

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.

scholarly journals A control architecture for continuous production processes based on industry 4.0: water supply systems application

2021 ◽  
Author(s):  
Edgar Chacón ◽  
Luis Alberto Cruz Salazar ◽  
Juan Cardillo ◽  
Yenny Alexandra Paredes Astudillo
Keyword(s):  
Water Supply ◽  
Industry 4.0 ◽  
Power Plants ◽  
Continuous Production ◽  
Continuous Process ◽  
Oil Refining ◽  
Water Supply Systems ◽  
Manufacturing Operations ◽  
Production Processes ◽  
Service Disruption

AbstractIndustry 4.0 (I4.0) brings together new disruptive technologies, increasing future factories’ productivity. Indeed, the control of production processes is fast becoming a key driver for manufacturing operations. Manufacturing control systems have recently been developed for distributed or semi-heterarchical architectures, e.g., holonic systems improving global efficiency and manufacturing operations’ reactiveness. So far, previous studies and applications have not dealt with continuous production processes, such as applications for Water Supply System (WSS), oil refining, or electric power plants. The complexity of continuous production is that a single fault can degrade extensively and even cause service disruption. Therefore, this paper proposes the Holonic Production Unit (HPU) architecture as a solution to control continuous production processes. An HPU is created as a holon unit depicting resources in a continuous process. This unit can detect events within the environment, evaluate several courses of action, and change the parameters aligned to a mission. The proposed approach was tested using a simulated model of WSS. The experiments described in this paper were conducted using a traditional WSS, where the communication and decision-making features allow the application of HPU. The results suggest that constructing a holarchy with different holons can fulfill I4.0 requirements for continuous production processes.

Digital Twins: Review and Challenges

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Maria G. Juarez ◽  
Vicente J. Botti ◽  
Adriana S. Giret
Keyword(s):  
Computer Science ◽  
Industry 4.0 ◽  
Multi Agent System ◽  
Agent System ◽  
Digital Twin ◽  
Digital Twins ◽  
Science Approach ◽  
Multi Agent ◽  
Current Operating

Abstract With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.

Sign in / Sign up

Export Citation Format

Share Document