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Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective.

Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and showthrough a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.

The purpose of the study was to investigate the application of Digital Twins (DTs) in sectors critical to societal functions

The integration of production automation drives innovation in manufacturing by enhancing efficiency, quality, and cost reduction. However, the capital requirements of conventional automation solutions hinder many manufacturing companies. Production Automation as a Service (PAaaS) emerges as a cost-effective alternative, offering improved flexibility and efficiency. Yet, adopting PAaaS faces challenges: a lack of expertise, awareness, and cultural resistance. This study explores PAaaS implementation in manufacturing, identifying its specific needs and challenges. Qualitative research across ten diverse manufacturing companies reveals two key drivers: technological advancement and evolving business models. It highlights four primary benefits—cost-effectiveness, flexibility, efficiency, and product quality. Simultaneously, it addresses five significant challenges—legacy system integration, cybersecurity, internet dependency, expertise gaps, and downtime risks. To aid early decision-making, the study proposes a framework covering drivers, benefits, challenges, and suitable strategies. This study contributes to the ongoing discussion on smart production and automation development by focusing on business model innovation and the pay-as-a-service approach.

Modern digital solutions are built around a variety of applications. The continuous integration of these applications brings advancements in technology. Therefore, it is essential to understand how these applications will behave when they run together. However, this can be challenging to interpret due to the increasing complexity of the execution details. One such fundamental detail is the utilization of shared cache as it goes hand in hand with the computation capacity of computer systems. Since cache utilization behavior is not simple enough to translate with few assumptions we have investigated if this complex behavior can be predicted with the help of machine learning. We trained the deep neural network with enough examples that represent the cache behavior when applications were running alone and when they were running concurrently on the same core. The Long Short-Term Memory (LSTM) network learns the entire execution period of each application in the training set. As a result, without running two applications together in reality, provided with the L1 cache misses of two applications (running alone), it can predict how the cache will look like if two applications wish to run together. The model returns a time series that reflects the cache behavior in concurrency. 

The transition towards sustainable electrification, particularly in the context of electric vehicles (EVs), necessitates a comprehensive understanding and effective management of battery circularity. With a plethora of EV models and battery variants, navigating the complexities of circularity becomes increasingly challenging. Furthermore, efficient fleet management emphasizes the necessity for robust data collection and analysis across diverse EVs to optimize battery value throughout its lifecycle. Advanced digital technologies play a crucial role in bridging informational gaps and enabling real-time connectivity, intelligence, and analytical capabilities for batteries. However, despite the potential benefits, the integration of circularity and digital technologies in the battery sector remains largely unexplored. Both circularity and digital technologies in the battery domain are still emerging, lacking conceptualization on their integration. To tackle these challenges, this paper advocates for the concept of smart battery circularity, which amalgamates advanced digital technologies with circular economy principles. The purpose of this paper is to enhance the conceptualization of smart battery circularity and elucidate the key knowledge areas necessary to facilitate it. The study identifies three critical knowledge areas essential for enabling smart battery circularity: digitally enabled circular business models, digital twin platforms for circular battery services, and smart battery performance monitoring. The sub-areas within each key knowledge area are also outlined. By delineating these knowledge areas, the study proposes an integrative framework, showcasing how these areas contribute to smart battery circularity both individually and collectively. The study offers insights to accelerate the development of initiatives aimed at establishing a sustainable and circular battery ecosystem, thereby advancing global efforts towards climate-neutral electrification. 

The world is facing a series of new societal and industrial challenges, such as continuously increasing costs in food and energy supply and a short supply of skilled labor. To solve these new challenges, operation, information, and communication technology is entering a new era with more focus targeted to cost reduction and energy savings. In this article, how innovative technologies including virtualization, low-code development, digital twins, and industrial agents can support and impact digital and green transitions is analyzed. In addition, how these technologies will help the security and sustainability of future industrial automation systems is also discussed.

The functional suitability of digital twin systems relies on accurately capturing, modelling, and exchanging data from their corresponding assets or processes. Consequently, achieving interoperability among various components and different digital twin systems is crucial. In the current landscape, characterized by various languages supporting diverse semantic models, achieving interoperability is a complex task. Achieving interoperability might involve translating diverse models, either peer-to-peer or through a central pivotal model. In this study, we propose a model-driven engineering approach that leverages higher-order transformations in conjunction with the Asset Administration Shell, acting as the pivotal model to tackle the interoperability challenges associated with digital twin systems. Higher-order transformations are a specific type of model transformation, characterized by their input and/or output being model transformations themselves. Our hypothesis is that such transformations would eliminate the need to manually craft multiple translations toward the Asset Administration Shell. In-stead, a single higher-order transformation would automatically generate these translations. We chose the Asset Administration Shell as our pivotal model, because it is widely recognized as a foundational element for application interoperability in Industry 4.0/5.0. We illustrate our approach through a vehicle use case represented using the Systems Modeling Language version 2 and consolidating this description into an Asset Administration Shell model. Hence, we showcase the applicability and suitability of our proposed approach. To the best of our knowledge, our work represents the first effort to address the translation of Systems Modeling Language version 2 into the Asset Administration Shell.

Digital twins involve the integration of advanced information technologies to create software replicas that control and monitor physical assets. Interoperability is an essential requirement in the engineering of digital twins. This paper is the first study analysing interoperability in digital twin software architectures in the manufacturing industry. We began with an initial set of 2403 peer-reviewed publications and after a screening process, we selected a final set of 21 primary studies. We identified the set of technologies used for data exchange and the level of interoperability achieved during such an exchange. We organised the results according to the ISO 23247 standard and the level of conceptual interoperability model.

Modern hardware and software are becoming increasingly complex due to advancements in digital and smart solutions. This is why industrial systems seek efficient use of resources to confront the challenges caused by the complex resource utilization demand. The demand and utilization of different resources show the particular execution behavior of the applications. One way to get this information is by monitoring performance events and understanding the relationship among them. However, manual analysis of this huge data is tedious and requires experts’ knowledge. This paper focuses on automatically identifying the relationship between different performance events. Therefore, we analyze the data coming from the performance events and identify the points where their behavior changes. Two events are considered related if their values are changing at ”approximately” the same time. We have used the Sigmoid function to compute a real-value similarity between two sets (representing two events). The resultant value of similarity is induced as a similarity or distance metric in a traditional clustering algorithm. The proposed solution is applied to different software applications that are widely used in industrial systems to show how different setups including the selection of cost functions can affect the results.