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Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems
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Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 23926

Special Issue Editor

Dr. Thomas Usländer

E-Mail Website
Guest Editor
Fraunhofer IOSB, Head of Department, 176131 Karlsruhe, Germany
Interests: smart production ecosystems; AI systems engineering; open software and system architectures;manufacturing as a service

Special Issue Information

Dear Colleagues,

The demands that have originally triggered the initiative Industrie 4.0 in Germany, such as higher flexibility, smaller lot sizes, or manufacturing on demand, are not only relevant for production plants within enterprise but also for the cooperation between enterprises. One example are networks of suppliers and supply chain management where overarching objectives, such as sustainability and resilience, are becoming increasingly important. This Special Issue asks for papers that describe the technological challenges and trends in this transition.

The following topics shall be addressed:

  • business models, use cases, and application scenarios;
  • open manufacturing as a service (Smart Factory Web);
  • role of and compliance with international IT standards;
  • usage of Industrie 4.0 concepts, e.g., asset administration shell;
  • cybersecurity risks and measures in production ecosystems;
  • AI systems engineering;
  • data sovereignty (needs, concepts, and technologies);
  • role of international initiatives (such as GAIA-X, International Data Spaces (IDS), and Catena-X).

Dr. Thomas Usländer
Guest Editor

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Published Papers (6 papers)

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25 pages, 1030 KiB  
Article
Smarter Evolution: Enhancing Evolutionary Black Box Fuzzing with Adaptive Models
by Anne Borcherding, Martin Morawetz and Steffen Pfrang
Sensors 2023, 23(18), 7864; https://doi.org/10.3390/s23187864 - 13 Sep 2023
Cited by 1 | Viewed by 1679
Abstract
Smart production ecosystems are a valuable target for attackers. In particular, due to the high level of connectivity introduced by Industry 4.0, attackers can potentially attack individual components of production systems from the outside. One approach to strengthening the security of industrial control [...] Read more.
Smart production ecosystems are a valuable target for attackers. In particular, due to the high level of connectivity introduced by Industry 4.0, attackers can potentially attack individual components of production systems from the outside. One approach to strengthening the security of industrial control systems is to perform black box security tests such as network fuzzing. These are applicable, even if no information on the internals of the control system is available. However, most security testing strategies assume a gray box setting, in which some information on the internals are available. We propose a new approach to bridge the gap between these gray box strategies and the real-world black box setting in the domain of industrial control systems. This approach involves training an adaptive machine learning model that approximates the information that is missing in a black box setting. We propose three different approaches for the model, combine them with an evolutionary testing approach, and perform an evaluation using a System under Test with known vulnerabilities. Our evaluation shows that the model is indeed able to learn valuable information about a previously unknown system, and that more vulnerabilities can be uncovered with our approach. The model-based approach using a Decision Tree was able to find a significantly higher number of vulnerabilities than the two baseline fuzzers. Full article
(This article belongs to the Special Issue Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems)
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21 pages, 1905 KiB  
Article
Increasing Interoperability between Digital Twin Standards and Specifications: Transformation of DTDL to AAS
by Carlos Schmidt, Friedrich Volz, Ljiljana Stojanovic and Gerhard Sutschet
Sensors 2023, 23(18), 7742; https://doi.org/10.3390/s23187742 - 7 Sep 2023
Cited by 16 | Viewed by 2956
Abstract
Although standards and specifications for digital twins aim to create interoperability in Industry 4.0, each standard has its own goals, focuses and representations for digital twins. This paper examines an approach to increasing interoperability between established digital twin specifications by transformation. Accordingly, several [...] Read more.
Although standards and specifications for digital twins aim to create interoperability in Industry 4.0, each standard has its own goals, focuses and representations for digital twins. This paper examines an approach to increasing interoperability between established digital twin specifications by transformation. Accordingly, several specifications are presented and requirements for transformation are examined. Following the feasibility analysis, a mapping between the Digital Twin Definition Language (DTDL) and Asset Administration Shell (AAS) was created. To examine the feasibility of this approach, the transformation was implemented and tested for a physical asset. This paper demonstrates that a generic mapping between DTDL and AAS can be applied for transformation in use cases where DTDL models are provided while AAS is required. Full article
(This article belongs to the Special Issue Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems)
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36 pages, 2771 KiB  
Article
Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach
by Muhammad Hammad, Rashad Maqbool Jillani, Sami Ullah, Abdallah Namoun, Ali Tufail, Ki-Hyung Kim and Habib Shah
Sensors 2023, 23(17), 7555; https://doi.org/10.3390/s23177555 - 31 Aug 2023
Cited by 23 | Viewed by 3870
Abstract
Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based [...] Read more.
Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization. Within the network-based manufacturing system (NBMS), mobile industrial robots (MiRs) are vital in increasing operational efficiency, adaptability, and productivity. However, with the advent of IoT-enabled manufacturing systems, security has become a serious challenge because of the communication of various devices acting as mobile nodes. This paper proposes the framework for a newly personalized customization factory, considering all the advanced technologies and tools used throughout the production process. To encounter the security concern, an IoT-enabled NBMS is selected as the system model to tackle a black hole attack (BHA) using the NTRUEncrypt cryptography and the ad hoc on-demand distance-vector (AODV) routing protocol. NTRUEncrypt performs encryption and decryption while sending and receiving messages. The proposed technique is simulated by network simulator NS-2.35, and its performance is evaluated for different network environments, such as a healthy network, a malicious network, and an NTRUEncrypt-secured network based on different evaluation metrics, including throughput, goodput, end-to-end delay, and packet delivery ratio. The results show that the proposed scheme performs safely in the presence of a malicious node. The implications of this study are beneficial for manufacturing industries looking to embrace IoT-enabled subtractive and additive manufacturing facilitated by mobile industrial robots. Implementation of the proposed scheme ensures operational efficiency, enables personalized customization, and protects confidential data and communication in the manufacturing ecosystem. Full article
(This article belongs to the Special Issue Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems)
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38 pages, 8606 KiB  
Article
Building a Digital Manufacturing as a Service Ecosystem for Catena-X
by Felix Schöppenthau, Florian Patzer, Boris Schnebel, Kym Watson, Nikita Baryschnikov, Birgit Obst, Yashkumar Chauhan, Domenik Kaever, Thomas Usländer and Piyush Kulkarni
Sensors 2023, 23(17), 7396; https://doi.org/10.3390/s23177396 - 24 Aug 2023
Cited by 12 | Viewed by 6480
Abstract
Manufacturing as a Service (MaaS) enables a paradigm shift in the current manufacturing landscape, from integrated production and inflexible, fragile supply chains to open production and flexible, robust supply chains. As part of this evolution, new scaling effects for production capacities and customer [...] Read more.
Manufacturing as a Service (MaaS) enables a paradigm shift in the current manufacturing landscape, from integrated production and inflexible, fragile supply chains to open production and flexible, robust supply chains. As part of this evolution, new scaling effects for production capacities and customer segments are possible. This article describes how to accomplish this paradigm shift for the automotive industry by building a digital MaaS ecosystem for the large-scale automotive innovation project Catena-X, which aims at a standardized global data exchange based on European values. A digital MaaS ecosystem can not only achieve scaling effects, but also realize new business models and overcome current and future challenges in the areas of legislation, sustainability, and standardization. This article analyzes the state-of-the-art of MaaS ecosystems and describes the development of a digital MaaS ecosystem based on an updated and advanced version of the reference architecture for smart connected factories, called the Smart Factory Web. Furthermore, this article describes a demonstrator for a federated MaaS marketplace for Catena-X which leverages the full technological potential of this digital ecosystem. In conclusion, the evaluation of the implemented digital ecosystem enables the advancement of the reference architecture Smart Factory Web, which can now be used as a blueprint for open, sustainable, and resilient digital manufacturing ecosystems. Full article
(This article belongs to the Special Issue Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems)
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21 pages, 2393 KiB  
Article
Exploring the Role of Federated Data Spaces in Implementing Twin Transition within Manufacturing Ecosystems
by Marko Jurmu, Ilkka Niskanen, Atte Kinnula, Jukka Kääriäinen, Markus Ylikerälä, Pauli Räsänen and Tuomo Tuikka
Sensors 2023, 23(9), 4315; https://doi.org/10.3390/s23094315 - 27 Apr 2023
Cited by 8 | Viewed by 3428
Abstract
Globally, manufacturing ecosystems are facing the challenge of twin transition, i.e., how to utilize digitalization for improving or transforming the sustainability of manufacturing operations. Here, operations refer widely to the upstream of manufacturing, while the entire product lifecycle also covers the downstream and [...] Read more.
Globally, manufacturing ecosystems are facing the challenge of twin transition, i.e., how to utilize digitalization for improving or transforming the sustainability of manufacturing operations. Here, operations refer widely to the upstream of manufacturing, while the entire product lifecycle also covers the downstream and end-of-life operations. Here, sustainability is understood to consider the impact of the product lifecycle at environmental, social, and governance (ESG) levels. In this article, we explore this progress through the digitalization concept of business-to-business data sharing, and through one example of a manufacturing ecosystem in Finland. We discuss the federated data space concept and the international data spaces (IDS) architecture as technological building blocks of twin transition, and report the first results from an industry−research shared-risk project. Semi-structured interviews and a diary-style reporting from an industry−research IDS proof-of-concept (PoC) experiment are presented and analyzed within a design science research method framework. The findings give the first indications that while data sharing is seen as important and increasing in relevance in industry, it is currently challenging for companies to see how an open standard architecture creates value beyond a single limited ecosystem view. We also highlight possible avenues for further research. Full article
(This article belongs to the Special Issue Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems)
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21 pages, 4833 KiB  
Perspective
On the Role of Digital Twins in Data Spaces
by Friedrich Volz, Gerhard Sutschet, Ljiljana Stojanovic and Thomas Usländer
Sensors 2023, 23(17), 7601; https://doi.org/10.3390/s23177601 - 1 Sep 2023
Cited by 13 | Viewed by 4380
Abstract
Industry 4.0 supports the vision of networked machines in decentralized production plants across the value chain. Hence, it requires highly connected partners exchanging relevant data about products, processes, and production resources. This paper proposes the usage of data spaces and digital twins to [...] Read more.
Industry 4.0 supports the vision of networked machines in decentralized production plants across the value chain. Hence, it requires highly connected partners exchanging relevant data about products, processes, and production resources. This paper proposes the usage of data spaces and digital twins to enable this Industry 4.0 vision and investigates the building blocks to realize a data space for Industry 4.0, e.g., the integration of digital twins inside the data space based upon the latest specification of the Industry 4.0 Asset Administration Shell. A prototypical implementation shows the feasibility of storing product carbon footprints inside a digital twin and sharing it over a data space with other partners. Full article
(This article belongs to the Special Issue Technological Challenges and Trends in Sensor-Based Smart Production Ecosystems)
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