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Cyber Physical Production Systems
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Cyber Physical Production Systems

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 51190

Special Issue Editor

Prof. Dr. Sebastian Thiede

E-Mail Website
Guest Editor
Chair of Manufacturing Systems, Faculty of Engineering Technology, Department of Design, Production & Management, University of Twente, 7522 LW Enschede, The Netherlands
Interests: sustainable manufacturing; cyber physical productions systems; planning and operation of manufacturing systems, life cycle costing (LCC); life cycle maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Without a question, digitalization is one of the major trends in manufacturing—typically associated with terms like Industry 4.0, smart factory or industrial internet—and will have a significant influence on the planning and control of future factories. Cyberphysical production systems (CPPS) are the technical core element of this “4th Industrial Revolution”. As indicated by the name, CPPS consist of a “physical” and an up-to-date “cyber” subsystem (often referred to as “digital twin”) based on continuous data acquisition. With this, decision support or even automatic control functionalities can be enabled. CPPS bear significant potential for improvement and can be applied for production machines, process chains or factories as a whole. Examples can be found in the context of process/machine control, maintenance, energy management, and quality management. However, it is often just singular approaches with a focus on specific CPPS elements or rather conceptual descriptions that can be found so far. Additionally, there is a need for case studies which prove the feasibility and give a clear assessment of benefits but also related drawbacks of CPPS solutions. Against this background, contributions to foster knowledge in those areas are of specific interest for this Special Issue.

Dr. Sebastian Thiede
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Innovative CPPS concepts and implementations on different levels of manufacturing (machine, process chain, factory)
  • Case studies on CPPS in industrial applications
  • Methods and tools for assessing the feasibility and performance of CPPS
  • CPPS towards sustainability in manufacturing, e.g. energy and resource efficiency
  • Business models bases on CPPS

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Published Papers (9 papers)

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Editorial

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2 pages, 164 KiB  
Editorial
Cyber-Physical Production Systems (CPPS): Introduction
by Sebastian Thiede
J. Manuf. Mater. Process. 2021, 5(1), 24; https://doi.org/10.3390/jmmp5010024 - 17 Mar 2021
Cited by 7 | Viewed by 3368
Abstract
Digitalization is a major change driver in manufacturing and is nowadays typically linked to terms like Industry 4 [...] Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)

Research

Jump to: Editorial

20 pages, 2747 KiB  
Article
Advances in Machine Learning Detecting Changeover Processes in Cyber Physical Production Systems
by Bastian Engelmann, Simon Schmitt, Eddi Miller, Volker Bräutigam and Jan Schmitt
J. Manuf. Mater. Process. 2020, 4(4), 108; https://doi.org/10.3390/jmmp4040108 - 13 Nov 2020
Cited by 20 | Viewed by 5969
Abstract
The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the [...] Read more.
The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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21 pages, 3938 KiB  
Article
Virtual Quality Gates in Manufacturing Systems: Framework, Implementation and Potential
by Marc-André Filz, Sebastian Gellrich, Artem Turetskyy, Jacob Wessel, Christoph Herrmann and Sebastian Thiede
J. Manuf. Mater. Process. 2020, 4(4), 106; https://doi.org/10.3390/jmmp4040106 - 9 Nov 2020
Cited by 26 | Viewed by 7298
Abstract
Manufacturing companies are exposed to increased complexity and competition. To stay competitive, companies need to minimize the total cost of quality while ensuring high transparency about process–product relationships within the manufacturing system. In this context, the development of technologies such as advanced analytics [...] Read more.
Manufacturing companies are exposed to increased complexity and competition. To stay competitive, companies need to minimize the total cost of quality while ensuring high transparency about process–product relationships within the manufacturing system. In this context, the development of technologies such as advanced analytics and cyber physical production systems offer a promising approach. This paper discusses and defines essential elements of virtual quality gates in the context of manufacturing systems. To support the planning and implementation of virtual quality gates, a morphological box is developed which can be used to identify and derive an individual approach for a virtual quality gate based on the specific characteristics and requirements of the respective manufacturing system. Moreover, the framework is exemplified by three case studies from various industries and resulting potential are discussed. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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15 pages, 3606 KiB  
Article
RFID Application in a Multi-Agent Cyber Physical Manufacturing System
by Maryam Farsi, Christina Latsou, John Ahmet Erkoyuncu and Geoffrey Morris
J. Manuf. Mater. Process. 2020, 4(4), 103; https://doi.org/10.3390/jmmp4040103 - 29 Oct 2020
Cited by 13 | Viewed by 3970
Abstract
In manufacturing supply chains with labour-intensive operations and processes, individuals perform various types of manual tasks and quality checks. These operations and processes embrace engagement with various forms of paperwork, regulation obligations and external agreements between multiple stakeholders. Such manual activities can increase [...] Read more.
In manufacturing supply chains with labour-intensive operations and processes, individuals perform various types of manual tasks and quality checks. These operations and processes embrace engagement with various forms of paperwork, regulation obligations and external agreements between multiple stakeholders. Such manual activities can increase human error and near misses, which may ultimately lead to a lack of productivity and performance. In this paper, a multi-agent cyber-physical system (CPS) architecture with radio frequency identification (RFID) technology is presented to assist inter-layer interactions between different manufacturing phases on the shop floor and external interactions with other stakeholders within a supply chain. A dynamic simulation model in the AnyLogic software is developed to implement the CPS-RFID solution by using the agent-based technique. A case study from cryogenic warehousing in cell and gene therapy has been chosen to test the validity of the presented CPS-RFID architecture. The analyses of the simulation results show improvement in efficiency and productivity, in terms of resource time-in-system. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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24 pages, 8440 KiB  
Article
Data-Driven Digital Twins for Technical Building Services Operation in Factories: A Cooling Tower Case Study
by Christine Blume, Stefan Blume, Sebastian Thiede and Christoph Herrmann
J. Manuf. Mater. Process. 2020, 4(4), 97; https://doi.org/10.3390/jmmp4040097 - 23 Sep 2020
Cited by 33 | Viewed by 6425
Abstract
Cyber-physical production systems (CPPS) and digital twins (DT) with a data-driven core enable retrospective analyses of acquired data to achieve a pervasive system understanding and can further support prospective operational management in production systems. Cost pressure and environmental compliances sensitize facility operators for [...] Read more.
Cyber-physical production systems (CPPS) and digital twins (DT) with a data-driven core enable retrospective analyses of acquired data to achieve a pervasive system understanding and can further support prospective operational management in production systems. Cost pressure and environmental compliances sensitize facility operators for energy and resource efficiency within the whole life cycle while achieving reliability requirements. In manufacturing systems, technical building services (TBS) such as cooling towers (CT) are drivers of resource demands while they fulfil a vital mission to keep the production running. Data-driven approaches, such as data mining (DM), help to support operators in their daily business. Within this paper the development of a data-driven DT for TBS operation is presented and applied on an industrial CT case study located in Germany. It aims to improve system understanding and performance prediction as essentials for a successful operational management. The approach comprises seven consecutive steps in a broadly applicable workflow based on the CRISP-DM paradigm. Step by step, the workflow is explained including a tailored data pre-processing, transformation and aggregation as well as feature selection procedure. The graphical presentation of interim results in portfolio diagrams, heat maps and Sankey diagrams amongst others to enhance the intuitive understanding of the procedure. The comparative evaluation of selected DM algorithms confirms a high prediction accuracy for cooling capacity (R2 = 0.96) by using polynomial regression and electric power demand (R2 = 0.99) by linear regression. The results are evaluated graphically and the transfer into industrial practice is discussed conclusively. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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22 pages, 5380 KiB  
Article
An Agent-Based System for Automated Configuration and Coordination of Robotic Operations in Real Time—A Case Study on a Car Floor Welding Process
by Sotiris Makris, Kosmas Alexopoulos, George Michalos and Andreas Sardelis
J. Manuf. Mater. Process. 2020, 4(3), 95; https://doi.org/10.3390/jmmp4030095 - 18 Sep 2020
Cited by 9 | Viewed by 3215
Abstract
This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and [...] Read more.
This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and configuration purposes. The ontological representation of the production environment is discussed. Using this framework, the manufacturing resources are capable of autonomously embedding themselves into the existing manufacturing enterprise with minimal human intervention, while, at the same time, the coordination of manufacturing operations is achieved without extensive human involvement. The specific framework was implemented, tested and validated in a feasibility study upon a laboratory robotic assembly cell with typical industrial components, using real data derived from a car-floor welding process. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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21 pages, 3495 KiB  
Article
Simulation-Based Multi-Criteria Optimization of Parallel Heat Treatment Furnaces at a Casting Manufacturer
by Thomas Sobottka, Felix Kamhuber and Bernhard Heinzl
J. Manuf. Mater. Process. 2020, 4(3), 94; https://doi.org/10.3390/jmmp4030094 - 17 Sep 2020
Cited by 9 | Viewed by 3484
Abstract
This paper presents the development and evaluation of a digital method for multi-criteria optimized production planning and control of production equipment in a case-study of an Austrian metal casting manufacturer. Increased energy efficiency is a major requirement for production enterprises, especially for energy [...] Read more.
This paper presents the development and evaluation of a digital method for multi-criteria optimized production planning and control of production equipment in a case-study of an Austrian metal casting manufacturer. Increased energy efficiency is a major requirement for production enterprises, especially for energy intensive production sectors such as casting. Despite the significant energy-efficiency potential through optimized planning and the acknowledged application potential for sophisticated simulation-based methods, digital tools for practical planning applications are still lacking. The authors develop a planning method featuring a hybrid (discrete-continuous) simulation-based multi-criteria optimization (a multi-stage hybrid heuristic and metaheuristic method) for a metal casting manufacturer and apply it to a heat treatment process, that requires order batching and sequencing/scheduling on parallel machines, considering complex restrictions. The results show a ~10% global goal optimization potential, including traditional business goals and energy efficiency, with a ~6% energy optimization. A basic feasibility demonstration of applying the method to synchronize energy demand with fluctuating supply by considering flexible energy prices is conducted. The method is designed to be included in the planning loop of metal casting companies: receiving orders, machine availability, temperature data and (optional) current energy market price-data as input and returning an optimized plan to the production-IT systems for implementation. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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20 pages, 4680 KiB  
Article
Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites
by André Hürkamp, Sebastian Gellrich, Tim Ossowski, Jan Beuscher, Sebastian Thiede, Christoph Herrmann and Klaus Dröder
J. Manuf. Mater. Process. 2020, 4(3), 92; https://doi.org/10.3390/jmmp4030092 - 11 Sep 2020
Cited by 55 | Viewed by 8985
Abstract
The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP [...] Read more.
The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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22 pages, 1782 KiB  
Article
Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective
by Mareike Dornhöfer, Simon Sack, Johannes Zenkert and Madjid Fathi
J. Manuf. Mater. Process. 2020, 4(3), 89; https://doi.org/10.3390/jmmp4030089 - 9 Sep 2020
Cited by 15 | Viewed by 7228
Abstract
The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only [...] Read more.
The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only possible if the production unit is smart and flexible. However, an existing factory cannot be transformed easily into a smart factory, especially not during operational mode. Therefore, designers and engineers require solutions which help to simulate the aspired change beforehand, thus running realistic pre-tests without disturbing operations and production. New product lines may also be tested beforehand. Data and the deduced knowledge are key factors of the said transformation. One idea for simulation is applying artificial intelligence, in this case the method of multi-agent-systems (MAS), to simulate the inter-dependencies of different production units based on individually configured orders. Once the smart factory is running additional machine learning methods for feedback data of the different machine units may be applied for generating knowledge for improvement of processes and decision making. This paper describes the necessary interaction of manufacturing and knowledge-based solutions before showing an MAS use case implementation of a production line using Anylogic. Full article
(This article belongs to the Special Issue Cyber Physical Production Systems)
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