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Integration of the Asset Administration Shell (AAS) for Smart Manufacturing: State-of-Art and Future Opportunities

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17 June 2024

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20 June 2024

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Abstract
Industry 4.0 (I4.0) epitomizes the nexus of technological evolution and manufacturing process enhancements, underpinned by four critical pillars: Cyber-Physical Systems (CPS), Digital Twins (DT), the Industrial Internet of Things (IIoT), and Cloud Computing (CC). Through the lens of the German Reference Architectural Model Industrie (RAMI) 4.0, these components are integral to fostering Smart Manufacturing (SM) practices. In conjunction, the Asset Administration Shell (AAS) facilitates seamless inter-company communication and enables adaptability to fluctuating market demands. This paper delves into the structural intricacies of AAS and SM within the RAMI 4.0 framework, unveiling three distinct implementation strategies. Furthermore, it meticulously scrutinizes challenges that hinder the full realization of I4.0 potential, such as interoperability, security concerns, and standardized communication protocols. By examining these models and identifying current barriers, the study illuminates pathways for future research, particularly in enhancing the integration of AAS and SM systems, ensuring robust security measures, and advocating for the development of universal standards. This exploration aims to contribute to the ongoing discourse on I4.0, proposing avenues for advancing manufacturing technologies and operational frameworks in alignment with the principles of RAMI 4.0.
Keywords: 
Subject: Engineering  -   Industrial and Manufacturing Engineering

1. Introduction

The onset of Industry 4.0 (I4.0) heralds a transformative era in manufacturing, characterized by the convergence of Cyber-Physical Systems (CPS), the Industrial Internet of Things (IIoT), Digital Twins (DT), and Cloud Computing (CC). This paradigm shift aims to facilitate seamless communication across systems through digital integration. Nevertheless, the path to achieving full integration is fraught with challenges due to system languages and protocol heterogeneity. The divergence in reference models exacerbates this issue, presenting a significant barrier to universal implementation.
This article draws upon the principles outlined in the Reference Architectural Model Industrie 4.0 (RAMI 4.0), introduced by the German Electrical and Electronics Manufacturers’ Association. RAMI 4.0, a service-oriented architectural framework, amalgamates a comprehensive collection of standards, practices, and benchmarks [1,2]. Central to this discussion is the Plattform Industrie 4.0 [3], an online repository that curates a wealth of resources on I4.0, including work templates, models, and standards. This platform is instrumental in advancing industry digitalization through the Asset Administration Shell (AAS) concept, which is crucial for the effective management of digital twins [4,5,6,7].
Emerging from the foundations of I4.0, Smart Manufacturing (SM) represents a sophisticated capability to process vast datasets, establish secure communication protocols, and define precise access controls for organizational data [7,8]. The hallmark of SM lies in its adaptability to evolving scenarios, proficiency in pattern recognition, and capacity to extrapolate and disseminate learning across system components. The synergy between AAS and SM propels the development of more integrated systems that align closely with I4.0 objectives, particularly the real-time monitoring and management of processes via CPS and IIoT.
Plattform Industrie 4.0 [3] plays a pivotal role in elucidating the nuances of AAS, including critical considerations for its deployment, adherence to standardized frameworks, and the seamless fusion of digital and physical realms. However, the inherent variability in manufacturing processes poses a substantial challenge to the standardization of SM system implementations. This paper aims to explore these challenges in-depth, offering insights into potential pathways to harmonize the integration of digital systems within the manufacturing sector, thereby unlocking new dimensions of efficiency and innovation.
The depiction in Figure 1 showcases a dynamic academic collaboration network within DT and SM studies, highlighting central figures such as Tao F., Qi Q., and Zhang H. as key contributors with extensive collaborative ties and impact. This network graph reflects the interdisciplinary essence of the domain, marked by the variety of authors and the breadth of their scholarly outputs. Pioneering contributions by Tao F. et al. [9] and Qi Q. et al. [10] lay the groundwork for DT technologies and their deployment in IM environments. Complementarily, the investigations conducted by Negri E. et al. [11], alongside Jain S. and Narayanan A. [12], offer in-depth analyses and novel approaches that enhance CPS integration within industrial frameworks. This scholarly interconnection fosters a rich exchange of ideas and approaches, driving forward the technologies pivotal to I4.0’s evolution.
Table 1 presents a succinct yet thorough summary of critical researchers in the DT and SM spheres. Tao F. stands out as a leading figure, with an impressive portfolio of 345 publications, accumulating 1732 citations ([10]), demonstrating extensive contributions and a significant impact on the sector. Xu X. is also noteworthy, with 333 scholarly works and 1587 citations ([15]), indicating a profound influence on cloud manufacturing and associated innovations. Although Qi Q. presents a more modest volume of 45 publications, the substantial citation count of 971 ([10]) underscores their research’s critical and pioneering nature in integrating DT with IM. These metrics reflect the authors’ prolific research activities and suggest the dynamic progression of IM, emphasizing the integration of DT, CC, and IIoT as fundamental. The contributions from these scholars provide a robust basis for future explorations and advancements within the domain.
The keyword analysis depicted in Table 2 and Figure 2 unveils essential aspects of DT and IM research, with "SM" emerging as a focal point. This indicates a significant trend towards utilizing digital advancements to refine manufacturing systems, as elaborated by Jain et al. [17]. The emphasis on "DT" technology, investigated by Qian et al. [26] for its critical function in replicating physical systems to boost efficiency, complements this trend. Furthermore, the spotlight on "I4.0" showcases the progression towards the fusion of digital and physical operations, a narrative extended by Kler et al. [32] through their insights on manufacturing resilience enhancement. These studies collectively mirror the extensive scope of research dedicated to pushing the boundaries of manufacturing technologies and frameworks, resonating with the principles of I4.0. The analysis underscores critical research trajectories and reflects the transformative trajectory of manufacturing ecosystems, underscored by the integration of DT technologies and IM concepts.
Drawing from the extensive body of literature on I4.0 and insights from Plattform Industrie 4.0 [40,41,42,43,44,45,46], this work delineates the key considerations necessary for the effective integration of AAS and SM. The structure of this article is meticulously organized to facilitate a comprehensive understanding of these considerations. Specifically, Section 2 delves into the core principles of AAS and SM through the lens of RAMI 4.0 [47,48,49,50,51], analyzing the general framework of AAS as proposed by Plattform Industrie 4.0 [3] and exploring the fundamental components of SM in the absence of a definitive model. Moreover, this section presents a comparative analysis of Intelligent Manufacturing (IM) and SM, elucidating their significance within the I4.0 paradigm.
The subsequent sections build upon this foundation, with Section 3 detailing the practical application of AAS and SM by considering three pivotal aspects: the I4.0 framework as per RAMI 4.0, the implementation models for AAS, and the essential elements of SM. Section 4 focuses on the intricacies of facilitating communication between AAS and SM, guided by the principles of RAMI 4.0. Finally, Section 5 is dedicated to discussing the findings and contemplating prospective research avenues. Through this structured exploration, the article aims to contribute to the ongoing discourse on integrating digital systems within the manufacturing industry, highlighting the potential pathways and challenges inherent in this endeavor.

2. Concepts and Structure

2.1. Asset Administration Shell AAS

In exploring systems within I4.0, a notable consistency in operational methodologies is observed [50,52,53]. Nevertheless, a predominant feature of these systems is the incorporation of specialized machinery equipped with proprietary software and hardware configurations. This diversity often culminates in communication barriers among components due to inherent incompatibilities, thus obstructing the collaborative synergy envisioned by I4.0. RAMI 4.0 introduces the AAS concept as an innovative abstraction layer to mitigate such discrepancies. Positioned between the machinery’s hardware and network interface, AAS aims to harmonize the interaction within industrial production environments [54]. AAS serves as the digital counterpart or digital twin of physical assets in I4.0, encapsulating a variety of sub-models within its structure [54].
These sub-models comprehensively delineate an asset’s pertinent information and functionalities, encompassing its attributes, characteristics, states, operational parameters, measurement data, and intrinsic capabilities [47,55]. AAS significantly enhances systems’ monitoring, control, and interoperability by managing specialized data packets. Conceptually, AAS is segmented into two primary components: the header and the body, as illustrated in Figure 3. The header facilitates the management of interactions between the AAS and the actual physical asset. In contrast, the body archives the digital representations, including sub-models and objects collectively known as Sub-AAS, which encapsulate the detailed digital footprint of the asset [49,55]. This detailed framework underpins a more streamlined approach to integrating diverse industrial components, overcoming the traditional barriers of heterogeneous systems. Through the deployment of AAS, I4.0 aspires to realize a level of standardization and interoperability that aligns with its foundational objectives, thereby propelling the manufacturing sector toward a more interconnected and intelligent future.
Within the framework of RAMI 4.0, AAS plays a critical role across multiple layers: the asset layer, communication layer, information layer, and function layer [55]. This multifaceted role of AAS is pivotal in facilitating a seamless flow from production to management, thereby enhancing system-wide collaboration. AAS achieves this by implementing a structured approach that simplifies the integration of assets across these layers. A vital feature of this approach is the ability to imbue each asset with distinct properties and functionalities, which are further categorized based on access levels. Consequently, while AAS encompasses comprehensive information about an asset, it strategically restricts data accessibility by organizational hierarchy and needs.
By integrating across various operational levels, AAS transcends traditional communication barriers, thereby streamlining the control and management of products throughout their lifecycle—from manufacturing to end-of-life preservation. This integration not only facilitates inter-system communication but also ensures the efficient oversight of products, marking a significant advancement in realizing I4.0’s objectives.

2.2. Smart Manufacturing

The conceptual landscape of SM systems remains diverse, with no universally accepted definition solidified within the scope of I4.0. Cyber-Physical Production Systems (CPPS) are often aligned with the essence of SM, characterized by their digital development and comprehensive Information and Communication Technology (ICT)-based integration [56]. The National Institute of Standards and Technology (NIST) in the United States delineates SM as an integrated, collaborative manufacturing ecosystem capable of real-time responsiveness to the evolving demands and conditions of the factory floor, supply network, and customer base [40,57]. Meanwhile, the International Organization for Standardization (ISO) interprets SM as an advanced manufacturing paradigm that enhances performance through the synergistic and intelligent application of processes and resources across cyber, physical, and human dimensions, facilitating product and service delivery. This definition further acknowledges SM’s role in fostering inter-domain collaboration within the value chains of organizations [58]. Illustrated in Figure 4a, the general concept of SM is depicted [57], highlighting the significance of data and data-driven decision-making within the digital domain. Figure 4b elaborates on the pillars of SM, which are aligned with the objectives of I4.0, including sustainability and the creation of globally connected, dynamic, and flexible value networks. SM’s operational model is predicated on the sharing of data, wherein each asset is cognizant of its past and anticipated future states within the production line. This paradigm, known as shared knowledge, underscores the necessity of SM for the successful implementation of I4.0 initiatives
In summary, SM represents the collaborative endeavor of systems to adaptively respond to changes and manufacture high-quality products leveraging data insights. This is achieved through the strategic employment of technologies such as IIoT, CPS, and CC, aiming to establish a connected, data-centric supply network [48,59]. This approach enhances operational efficiency and productivity and propels the manufacturing industry toward a more sustainable and innovative future.

2.3. Intelligent Manufacturing and Smart Manufacturing: A Comparison

While at first glance, IM and SM may appear linguistically interchangeable, a nuanced distinction exists between these concepts, mainly when considered within the context of I4.0. SM is inherently designed to foster industry-wide collaboration [60,61], characterized by its dynamic, non-linear progression over time and its capacity for adaptation through the acquisition and dissemination of knowledge. Conversely, IM is predicated on a more systematic approach, emphasizing seasonal production tailored to specific environmental conditions. Its adaptability is primarily driven by data analytics rather than collaborative knowledge sharing [60,61]. The evolution from IM to SM signifies a response to the advancing needs of the manufacturing sector, with SM representing an enhanced iteration of IM, infused with intelligent technologies such as the IIoT, CPS, CC, and Big Data. These technologies are the linchpins of I4.0, facilitating a transition that leverages data management and Artificial Intelligence (AI) to morph traditional IM practices into more sophisticated, interconnected SM ecosystems [62,63]. The synergy between AAS and IM is less pronounced, given IM’s emphasis on integrating manufacturing processes and systems at varying degrees of AI application [63,64]. In contrast, SM’s operational ethos is underpinned by the deployment of CPS and IIoT [63,65], which are instrumental in enabling seamless communication across systems. This paradigm shift is visually represented in Figure 5, illustrating the transformation of traditional manufacturing processes into a cohesive SM framework. This integration facilitates unprecedented monitoring, control, and adaptability levels across different organizational tiers, effectively overcoming the traditional barriers associated with the physical oversight of manufacturing operations.

3. Implementation

I4.0 introduces a transformative digital landscape for process development, leveraging cutting-edge industrial technologies such as AI that emphasize learning and the transfer of knowledge between systems [3]. SM underpins this revolution by enhancing manufacturing processes through DT, a pivotal element that enables real-time quality control and significantly reduces the production of defective parts [56,61].

3.1. Industry 4.0 Based on RAMI 4.0

The transition to I4.0 necessitates digitizing assets alongside a unified communication protocol. To spearhead this shift, Plattform Industrie 4.0 was established as a multidisciplinary coalition of industrialists, policymakers, labor representatives, and scholars, aiming to facilitate a comprehensive exchange across all societal sectors [50,52]. A critical distinction between the operations of Industry 3.0 (I3.0) and I4.0 is the enhanced communication across various levels of manufacturing, as illustrated in Figure 6.
The RAMI 4.0 mandates standardization across these levels to ensure a cohesive implementation [5,7,55,63]. This standardization encompasses two essential prerequisites: (i) the establishment of structured communication frameworks and (ii) the creation of a comprehensive common language characterized by its unique signs, alphabet, vocabulary, syntax, grammar, semantics, pragmatics, and cultural norms. This initiative addresses the inherent limitations of natural language processing by AI systems, which often struggle to interpret context [66].
Implementing I4.0, guided by the principles of RAMI 4.0 (refer to Figure 7), fosters a seamless integration between the physical and digital realms. Plattform Industrie 4.0 facilitates this integration by adopting standardized templates and communication protocols delineated by RAMI 4.0. This strategic alignment permits the real-time monitoring and modification of manufactured products and solidifies the foundation for SM practices, heralding a new era of interconnected and intelligent manufacturing systems.

3.2. Asset Administration Shell (AAS)

I3.0 leverages computer-aided manufacturing to generate CNC codes, a process that is influenced by the software or machine tool. AAS emerges as a solution, advocating for a uniform digital ecosystem facilitating industry-wide communication in a common language. I4.0’s blueprint envisions extensive digitization, necessitating that every physical asset be mirrored in the digital sphere by a uniquely identifiable digital twin [67,68]. This digitalization is underpinned by a technology-agnostic metamodel and various technology-specific serialization mappings like XML, JSON, or OPC UA, with content delineation provided by domain-specific sub-model templates [47].
The distinction between digital models of assets and DT is often blurred in literature [4,6,8,42,46,66,67,69,70]. While this conflation is manageable within the I4.0 context, from a RAMI 4.0 perspective, it introduces challenges. Despite existing literature on AAS implementation [71,72], a universally applicable and easily replicable standard solution methodology remains elusive [69].
AAS deployment necessitates defined communication frameworks. Plattform Industrie 4.0 contributes insights on semantics and standardized identifiers [73,74,75], enriching the understanding of fundamental parameters crucial for AAS communication and identification [1,76,77], including the mandate that each AAS possess a unique registration code [66].
This paper presents three ways to implement an AAS (see Figure 8) based on Plattform Industrie 4.0 recommendations [3]. The first two ways correspond to descriptions available in the literature, while the third corresponds to this author’s opinion based on the information available on AAS within the Plattform Industrie 4.0.
The initial model proposes the creation of three distinct instances within the AAS framework: one dedicated to external products, another to in-house manufactured goods, and a third centered on end-users [70]. These instances cater to different facets of the I4.0 ecosystem: the first enhances inter-company communication, the second manages internal industrial processes and quality control, and the third focuses on aspects of the asset’s commercialization. Such a structure allows for a comprehensive AAS that encapsulates sub-AAS, ensuring information access is tiered according to relevance and sensitivity. Conversely, the second model outlines a phased approach to AAS integration, comprising, i) identifying asset variables, ii) modeling the AAS, and iii) establishing connectivity between the AAS and the physical world [69,76]. The initial phase emphasizes the importance of expert involvement for accurate variable identification, which is pivotal for defining the asset’s characteristics. Subsequent modeling leverages these variables to construct a digital twin, utilizing templates provided by Plattform Industrie 4.0 for enhanced accuracy [78]. The final stage focuses on creating databases and deploying IIoT solutions for seamless digital-physical integration. This methodology prioritizes detailed asset management within the company but overlooks stratified information access, potentially compromising data confidentiality.
The third model aligns with the objectives of I4.0 while safeguarding asset confidentiality through a six-stage implementation process: 1) pinpointing operationally significant asset variables for the company or external entities; 2) defining the asset’s characteristics based on these variables; 3) delineating and allocating access tiers to asset information; 4) constructing the asset instance; 5) developing sub-AAS predicated on the defined properties and access level allocations; and 6) instituting communication protocols both digitally and with the physical asset. Subsequently, IIoT frameworks are deployed for real-time observation and management utilizing CPS. This model serves as a detailed roadmap for AAS deployment aimed at curtailing superfluous data production by assets. It initiates with the discernment of asset variables that hold significance for the enterprise’s operational needs. Given the industry-specific relevance of certain asset variables, this model necessitates a comprehensive review to ascertain all pertinent variables for internally produced assets. This variable identification phase is critical for establishing the asset’s properties, during which information is classified for private or public consumption, ensuring precise and secure asset management.
Determining the type of information is critical for establishing information access levels. Specific data is deemed confidential even within a single organization, underpinning the unique operations and product differentiation strategies. Creating an asset’s digital twin, or AAS, leverages these determinations, forming sub-AAS that encapsulate specific behaviors and characteristics. The development of both AAS and sub-AAS necessitates adherence to I4.0’s standardized semantics and identification protocols to ensure coherence and interoperability.
Furthermore, this information should be accessible online, requiring the implementation of secure and efficient communication protocols to safeguard and facilitate data exchange. Two distinct communication strategies are essential: one internal to the organization, necessitating robust protocols to mitigate potential vulnerabilities within digital frameworks; the other, bridging the AAS with SM systems, must be tailored to manage the volume of data processed by the company, ensuring the capability for real-time data exchange. This dual approach underscores the importance of security and agility in the digital landscape of I4.0, enabling seamless and safe interactions across various levels of the manufacturing ecosystem.

3.3. Smart Manufacturing

SM embodies a comprehensive value-generation continuum extending from design through production, logistics, to service, engaging a variety of stakeholders [41,56]. The deployment of SM systems demands a more substantial endeavor than AAS integration, attributed to the necessity for advanced equipment and the employment of CPS for process monitoring and control, alongside the utilization of learning algorithms and data management strategies. These technologies, essential to the SM framework, are depicted in Figure 9.
The primary aim of SM is to establish effective communication with AAS, necessitating real-time data acquisition for the oversight and regulation of manufacturing operations. The quintessential objective of SM implementation is to attain adaptability to changes, facilitated by the systems’ inherent capacity for learning and knowledge dissemination. This adaptability underscores the intent to streamline the integration of AAS into diverse systems, enhancing the transferability of these implementations across varied operational contexts [46].
The formulation of SM systems lacks a universal protocol, with its establishment rooted in the principles of I4.0, particularly focusing on sustainability. The AAS seeks to orchestrate a standardized digital blueprint of industrial CPSs, consolidating all data accrued throughout their lifecycle and facilitating real-time evaluation and management of physical entities [45]. It’s advised that SM system implementation be aligned with the value chain dynamics, emphasizing the necessity for seamless equipment communication.
The integration of cyber and physical realms constitutes a crucial foundation for SM [41]. Serving as the core of SM, it also provides the infrastructure necessary for establishing IIoT communication protocols. CPS’s primary role is to process data through pre-defined mathematical models. In synergy with AAS, it enables the simulation of atypical scenarios, thus enriching the data repository available. The efficacy of SM hinges on the ability to analyze voluminous datasets in real-time, sourced from disparate stages along the value chain [79].
A comparative analysis of CPSs and contemporary manufacturing equipment reveals a commonality in their reliance on sensors and processors. The triumph of SM is contingent upon the availability of durable, energy-efficient, cost-efficient smart sensors and processors, complemented by wireless network interfaces [79]. Within the I4.0 architecture, the necessity for inter-system communication escalates, predicated on the meticulous collection, analysis, and secure dissemination of high-fidelity data, underscoring the importance of international standards in this realm [79].

4. Communication between AAS and Smart Manufacturing

SM thrives on machine-to-machine (M2M) communication, facilitating seamless data exchange and interaction among manufacturing components [77]. Essential to this ecosystem is the interface with AAS, necessitating a robust communication framework that bridges the physical and digital domains. A prominent challenge in this context is asset communication, where operational conditions often hinder effective data transmission. Despite advances in addressing network connectivity issues, existing technologies fall short of enabling flexible and uninterrupted communication across diverse machinery [40,72,77].
In alignment with RAMI 4.0, the interaction between AAS and SM leverages specific communication protocols, with Plattform Industrie 4.0 serving as a pivotal intermediary. Figure 7 delineates I4.0 components, detailing access layers and the integration of communication protocols to facilitate comprehensive connectivity. While various communication frameworks are explored within scholarly works, many are tailored to niche applications [54,67,69,74]. To date, IEC 62541 (OPC UA) emerges as the sole technology fully compliant with I4.0 specifications, offering robust M2M and machine-to-system (M2S) communication capabilities [67,80].
Figure 10 illustrates a network-wide communication protocol, enabling efficient data transmission courtesy of AAS’s role as a unifying structure for asset information exchange [67]. This protocol, applicable across the diverse AAS implementation strategies discussed, facilitates the assignment of distinct information access levels, thus ensuring data security and integrity within the I4.0 framework.

5. Discussion and Future Opportunities

I4.0 emerges as a transformative operational paradigm for the industrial sector, anchored in adopting advanced technologies such as CPS, IIoT, DT, and CC. The discourse is tailored to explore the integration of SM and AAS within the RAMI 4.0 architecture, given manufacturing’s pivotal role in the industry. A conceptual framework has been developed based on existing scholarly research to guide the integration and implementation of standardized models for SM system deployment in I4.0 contexts under the RAMI 4.0 schema. This framework serves as a foundational guide for subsequent, more detailed investigations.
The diverse and complex nature of industrial manufacturing processes presents a significant challenge in formulating a universal standard for SM system standardization and implementation under the RAMI 4.0 blueprint. Efforts are underway within the academic and professional communities to devise reference models to harmonize sub-models tailored to various asset aspects [62,67]. Although preliminary models have emerged within the literature, a comprehensive, standardized sub-model framework remains elusive [4,67,69,71,80,81].
This narrative has encapsulated an analysis of SM systems, their structural intricacies, and the modalities of AAS integration within the I4.0 milieu as proposed by the German Electrical and Electronic Manufacturers’ Association [47,50]. While alternative I4.0 models, such as those proposed by the U.S. and China, are documented [49,52,54], their development and proliferation lag behind the more extensively researched German model. The absence of a universally recognized reference model impedes the global standardization necessary for international trade. This gap underscores the potential for comprehensive studies on I4.0 reference models and their associated communication frameworks.
A formidable barrier to I4.0 adoption is the resource constraint many organizations face, particularly in procuring state-of-the-art equipment, implementing CPS, managing data, and training staff. This challenge disproportionately impacts small- and medium-sized enterprises (SMEs) and is particularly acute in developing regions where industries may still operate within an I2.0 framework. Moreover, the shift towards a digitalized future introduces additional data management and cybersecurity complexities. There is a pressing need to develop adaptive security protocols to mitigate emergent threats within this evolving digital landscape.
In summary, the discourse encapsulated herein offers a preliminary exploration into the integration of SM and AAS under the aegis of RAMI 4.0. This marks the inception of a dialogue aimed at bridging the existing gaps in standardization, resource allocation, and security within the I4.0 paradigm. As the industry gravitates towards this new model, concerted research, development, and policy formulation efforts will be paramount in addressing these challenges and facilitating a smooth transition to a more interconnected, efficient, and secure manufacturing ecosystem.

6. Conclusions and Future Works

This manuscript has delved into the current state of Asset Administration Shell (AAS) and its deployment within the Smart Manufacturing (SM) framework, revealing opportunities for enhancement in the existing models detailed in scholarly literature. A novel implementation model for AAS, adhering to the guidelines set forth by the Reference Architectural Model for Industrie 4.0 (RAMI 4.0), has been introduced to address these gaps. This model accentuates the need for resilient systems equipped with network connectivity to facilitate real-time data processing and exchange to foster system collaboration and cultivate a dynamic, adaptable value network.
Industry 4.0 (I4.0) aims to revolutionize the industrial landscape by digitalizing processes, creating an environment wherein systems are interoperable, thus mandating the adoption of standardized communication protocols. These protocols are crucial not only for formulating and managing AAS but also for bridging the physical and digital realms, ensuring seamless data flow and integration. The model proposed for AAS creation and implementation within the SM ecosystem is designed to set a benchmark, identifying key asset attributes while safeguarding data confidentiality. Nevertheless, the elaboration of communication protocols and the precise delineation of information access levels emerge as pivotal areas for subsequent inquiry.
Building on this foundation, the model aims to streamline the integration of AAS with SM systems, underscoring the importance of advanced, interconnected frameworks that support the extensive data demands and complex interactions characteristic of I4.0 environments. By highlighting the criticality of secure, efficient data exchange and system interoperability, this work not only lays the groundwork for future explorations into the development of comprehensive, universally applicable standards and protocols, but also paves the way for the practical application of AAS in SM. These efforts will not only enhance the practical application of AAS in SM but also contribute to the broader objective of achieving fully digitalized, intelligent manufacturing processes that are at the heart of the I4.0 vision.
Future work will focus on refining and expanding the communication protocols and access level definitions within the AAS implementation model for SM, addressing the integration challenges highlighted in the I4.0 environment. This will involve the development of more nuanced, secure, and efficient methodologies for data exchange and system interoperability, laying the groundwork for a comprehensive set of standards that facilitate seamless interaction across diverse industrial platforms within the RAMI 4.0 framework.

Author Contributions

Conceptualization, methodology, software, resources, validation, formal analysis, Figueroa E., Pavon W., Cavopiña H. and Mideros D.; investigation, writing–original draft preparation, Figueroa E., Pavon W., Cavopiña H. and Mideros D.; writing–review and editing, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

Universidad de las Fuerzas Armadas (ESPE) and Universidad Politecnica Salesiana funded this research.

Data Availability Statement

Data are contained within the article.

Acknowledgments

This paper presents partial results of the project among Universidad de las Fuerzas Armadas (ESPE) and Universidad Politecnica Salesiana.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Network visualization of author collaborations in the field of Digital Twins and Smart Manufacturing, highlighting key researchers and their interconnections based on the co-authorship of publications
Figure 1. Network visualization of author collaborations in the field of Digital Twins and Smart Manufacturing, highlighting key researchers and their interconnections based on the co-authorship of publications
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Figure 2. Occurrences of keywords in digital twins and smart manufacturing research.
Figure 2. Occurrences of keywords in digital twins and smart manufacturing research.
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Figure 3. General structure of an Administration Shell, [49].
Figure 3. General structure of an Administration Shell, [49].
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Figure 4. a) General concept of a smart manufacturing enterprise. b) Six pillars of smart manufacturing [57].
Figure 4. a) General concept of a smart manufacturing enterprise. b) Six pillars of smart manufacturing [57].
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Figure 5. Visualization of traditional manufacturing compared with smart manufacturing, [58].
Figure 5. Visualization of traditional manufacturing compared with smart manufacturing, [58].
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Figure 6. Industry 3.0 and Industry 4.0 structure models [51].
Figure 6. Industry 3.0 and Industry 4.0 structure models [51].
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Figure 7. Reference Architectural Model Industrie 4.0 (RAMI 4.0) [51].
Figure 7. Reference Architectural Model Industrie 4.0 (RAMI 4.0) [51].
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Figure 8. AAS implementation from Plattform Industrie 4.0 [78].
Figure 8. AAS implementation from Plattform Industrie 4.0 [78].
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Figure 9. Visual representation of all characteristics and technologies that can define an SM [65].
Figure 9. Visual representation of all characteristics and technologies that can define an SM [65].
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Figure 10. Secure connection and communication on I4.0 [66].
Figure 10. Secure connection and communication on I4.0 [66].
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Table 1. Overview of the Most Prolific Authors in Digital Twins and Smart Manufacturing Research.
Table 1. Overview of the Most Prolific Authors in Digital Twins and Smart Manufacturing Research.
Author Publications Number Citation Citations Number
Tao.,F. 345  [9] 1732
Xu.,X. 333  [15] 1587
Zhang.,H. 134  [15] 30
Qi.,Q. 45 [10] 971
Liu.,J. 44 [16] 82
Table 2. Frequency of Key Index Keywords and Representative Papers in Digital Twins and Smart Manufacturing Research.
Table 2. Frequency of Key Index Keywords and Representative Papers in Digital Twins and Smart Manufacturing Research.
Keyword Occurrences Representative Papers
Smart Manufacturing 358  [17,18,19]
Manufacture 176  [20,21,22]
Flow Control 172  [20,23,25]
Digital Twin 136  [26,28,28]
Embedded Systems 109  [26,29,30]
Industry 4.0 99  [23,32]
Cyber Physical System 83  [33,33]
Internet of Things 64  [35,36]
Decision Making 63  [37,38]
Life Cycle 59  [22,39]
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