The foundation of any digital data exchange in engineering is a common understanding of the vocabulary used. Ideally, a single, global, and harmonized semantic standard would define all necessary terminology. However, a variety of well-established and valuable semantic standards — such as ECLASS, IEC CDD, SI units, and numerous proprietary dictionaries — have evolved over time. Different engineering tools, user communities, and organizations typically support only a subset of these standards, which significantly hampers seamless data exchange. This contribution introduces a concept based on AutomationML that enables the vendor-agnostic modelling of mappings between existing semantic standards for interconnecting vocabularies across a heterogeneous semantic landscape and for publishing these mappings for global reuse. The implementation is publicly available to all interested parties.

The digital transformation in engineering requires standardized information models to close gaps in digital value chains and to increase engineering efficiency. Despite the existence of several digital data models for engineering, their reuse is hindered by a lack of robust, guided mechanisms for sustainable development and publication. While the IT sector widely leverages Git-based workflows for sharing and collaborative software development, engineering data modelers have yet to adopt similar practices effectively. Our requirements and gap analysis reveals that, while services like Microsoft GitHub offers a robust starting point for collaborative and efficient modeling, more specialized mechanisms are required to effectively adapt Git-like workflows to the unique needs of engineering models. In response, we propose a novel method and an implementation that combines the capabilities of GitHub’s open-source collaboration strengths with engineering-specific enhancements as versioning and flexible deployment across both open and closed platforms. Interested readers are invited to explore and test the presented solution.

In the automation industry, concepts such as Digital Twins, representing virtual counterparts of physical assets, enhance efficiency, sustainability, and economic viability by enabling e.g., optimizations, advanced monitoring, and predictive maintenance. To achieve this, Digital Twins rely on models (including simulation models), which in turn depend on data describing the current state of the physical asset. As connectivity increases, the available data extends beyond the data of the sensors of the asset itself. It extends to data from the surrounding environment and even beyond. This introduces new challenges for Digital Twins and their models in terms of data integration. In previous research it was shown that current implementations are not adequately prepared for this scenario. Currently, no analysis in the literature addresses how interaction within a connected environment alters the requirements for Digital Twins. Therefore, this paper explicitly identifies the emerging challenges associated with extending the information acquisition scope of Digital Twins beyond the immediate physical asset. This is accomplished through a systematic analysis of potential scenarios that consider the inclusion of external data. Our analysis reveals several new requirements, such as the need for an extended description of the model’s purpose and the specific requirements for the data input. Digital Twins must possess enhanced capabilities to identify appropriate data sources within a dynamic production environment, as well as methods to manage multiple, potentially redundant data streams effectively. Through this analysis, we aim to highlight potential research directions for Digital Twins in the automation industry and advocate for the integration of interconnectivity into Digital Twin systems.

Magnetostrictive Position Sensors (MPS), widely used for accurate displacement and velocity measurements, operate based on the Time-of-Flight (ToF) of structure-borne ultrasonic waves induced by magnetic interactions. However, their measurement accuracy can be significantly affected by External Magnetic Interference (EMI), which distorts the characteristic waveforms essential for precise ToF estimation. This study investigates the abilities of a Self-x concept to improve measurement accuracy by the integration of hybrid artificial intelligence techniques (Hybrid-AI) for the classification and mitigation of EMI-induced errors in MPS. The proposed approach is particularly suited for industrial environments characterized by diverse EMI sources. An additional external sensor is not required. Experimental evaluations confirm a significant improvement by self-monitoring and self-adjustments.

Efficiency and flexibility are continual demands on future production systems, and modularization has emerged as a strategy to address these challenges. In the process industry, the Module Type Package (MTP) standard was introduced to simplify the integration and configuration of modular production systems. Transferring the MTP technology to other indus-trial domains is an active research field. This paper describes how the MTP can be used to integrate robotic systems into mod-ular manufacturing plants without compromising the MTP standard. This fills a gap in existing literature and provides promising scientific and commercial opportunities. The study presents a layered concept encompassing all necessary compo-nents for the integration of robotic systems, from the robot's control to the orchestration of the modular plant. A prototypical implementation demonstrates the feasibility of the proposed ap-proach, successfully performing a typical robotic task, a Pick-and-Place operation, using MTP-based services. The imple-mented services, though representing basic robot functionali-ties, lay the groundwork for more complex services or nested service structures. In conclusion, this research contributes to the understanding of the potential integration of robotic systems in MTP-based modular production environments, paving the way for more sophisticated applications, and addressing open questions for future exploration. The prototypical implementation is publicly available on GitHub.

In the era of Industry 4.0, data is becoming increasingly vital for future manufacturing and digital twin applications. Currently, primarily asset-created data is utilized, while the potential benefits of incorporating external data from connected production systems remain untapped. This underutilization is largely due to the complex and time-consuming nature of integrating external data. Automating this process could significantly improve efficiency and unlock valuable insights for more effective decision-making. In this paper, we present the requirements for a system aimed at automating the integration of external data, considering factors such as data heterogeneity and the need to support diverse communication protocols. We further introduce an assistant system that facilitates this automated integration, enhancing manufacturing processes and digital twin applications. Furthermore, we provide a prototypical implementation of the assistant system in the context of a collaborative robot utilizing information from its surroundings obtained through external camera and LiDAR imaging. By addressing the challenges of external data integration, this research contributes to the advancement of data-driven manufacturing and the optimization of digital twin technologies.

Graduate students face great non-technical challenges during their PhD. Also supervisors can struggle in effective multi-PhD management. The present study explores the utilization of Design Thinking and business canvas approaches in different phases of a PhD to address these challenges. The state-of-art is summarized and the Business Model Canvas–as most common Design Thinking method in industrial practice was evaluated in workshops with students and was subsequently translated to PhD terminology. The study closes with recommendations for students and supervisors showing how these methods can help them to survive the PhD- e.g. in topic identification, innovation management or research communication.

Ideally, modern modular production plants are controlled via services with uniform interfaces and state machines. But in detail, services are of diverse nature. Due to this diversity, orchestration systems face a challenge of correctly "understanding" the available services and recognizing how and when these services can be used. This paper identifies fundamental service properties, enabling the categorization of services. Furthermore, the authors propose to assign an information model containing the service properties to each service. Consequently, an orchestration system can independently recognize, interpret, and accordingly coordinate the information about service properties and variants; they become explorable. Orchestration systems are thus put in a position to deal explicitly with different services of diverse control methods on their own.

The Digital Twin is one of the future key technologies of digitization and Industry 4.0. Through coupling the vast amount of available static and dynamic data about a physical asset with intelligent software functions, it aims for simplifying the increasingly complex functions and interconnections of automation systems. This makes it necessary to provide the Digital Twin with updated and high-quality data about the physical asset. However, little attention is paid to data not generated by the asset itself but from external data sources. This is partly due to a lack of methodologies for identifying and evaluating external data relevant for a specific Digital Twin. The contribution of this work is a categorization of data for the Digital Twin and a methodology to support the identification of data from outside of the physical asset. The work-in-progress refers to the context of industrial automation and is to be validated in the future in laboratories containing discrete manufacturing facilities at Pforzheim University and the University of Stuttgart.

The increasing networking of Industrial Control Systems (ICS) requires effective security measures. To still be able to influence the design of automation systems, security measures must be defined early enough during the ICS engineering workflow, which is often called "security by design". This paper presents existing security by design approaches for ICS and clusters them with respect to how they integrate security into existing engineering workflows. Because one major shortcoming of existing approaches is their dependence on the existing engineering workflow, an improved security by design integration mechanism is proposed, where "security decision points" can be integrated flexibly into any ICS engineering workflow.

Process engineers design industrial process plants using piping and instrumentation diagrams (P&IDs). Today, data analysts who diagnose plant disturbances based on historical signal trends usually analyze these complex diagrams manually on paper, which is time-consuming and error-prone. In the last 15 years, researchers have thus proposed several approaches and tools to turn these diagrams into machine-readable models that can be processed by software tools. Yet, these tools lack sophisticated query interfaces and intuitive visualizations. We propose the method TOPNAV to navigate plant topology models and aid data analytics. The method supports systematic searching for elements and paths in topology models and feeding the results into analytical tools to facilitate statistical analyses. In a user study, an up to 90% time reduction was observed compared to manual P&ID analysis, while reducing errors significantly.

This contribution pursues the approach to extend real and operative plant components by virtual elements to improve efficiency, safety, and reliability in the automation engineering process. Although some research has been done in the field of mixing real and virtual representations of different plant components, the presented Virtual-In-The-Loop approach promises a wide range of new applications. To enable a systematic investigation of its possibilities and limitations, we present a first-time categorisation of the opportunities for mixing real and virtual plant components and interfaces. Furthermore, we propose a terminology that enables the unambiguous naming of the different forms of mixtures. The results are intended to serve as an orientation and classification guide for future work in the field of Virtual-In-The-Loop Engineering.

Due to the distributed architecture of cyber-physical production systems (CPPS), the design of automation software is difficult and error-prone. It requires process domain-specific knowledge and a service-oriented approach to be able to design a requirement-specific solution. Previous software design methods for CPPS have several drawbacks. Either these insufficiently take into account the properties of distributed systems or, in the case of Domain-Specific Languages (DSL), their focus lies on interaction logic rather than on production-specific specialties. Similarly, the possibilities for using information from the Asset Administration Shell (AAS) have so far not been integrated. Therefore, we propose a new service-oriented hierarchical DSL consisting of layers with different levels of abstraction, allowing successive and distributed software design with different degrees of detail. Hence, both process engineers and software engineers can work together during the software developing process. By incorporating information from the AAS, a static and also dynamic parameterization of the individual DSL services becomes possible. Finally, a case-study in the field of process engineering demonstrates the advantages and the applicability of the proposed approach.

The basis for electronic data exchange between manufacturers and system integrators of automation components such as sensor devices is the availability of standardized data models and an exchange data format for such automation components. These models need to cover the mechanical, electrical and sensing properties of the sensor device. This paper describes the results of the work of an industrial consortium within the AutomationML association providing a generic modeling methodology for automation components including electrical interfaces as well as proposals for modelling the detection properties of a sensor. It is also shown how the semantics of attributes are defined and how proprietary naming conventions can be easily integrated.

Modular process plants enable the process industry to adapt to volatile and fluctuating markets. The preferred process technology concept for these market scenarios is realized through combining pre-engineered process equipment assemblies (PEA) from different manufacturers with standardized interfaces. While the flexibility gain for this type of plants is high, the design of the safety systems of these modular plants in terms of exchangeability and compatibility is challenging. One solution could be the development of a safety-interface for PEAs to create flexible and scalable safety systems. The principles of modular basic process control are analyzed and requirements with respect to safety design are discussed. The authors provide a differentiation of the intramodular safety concept of a single PEA from the intermodular safety concept of a complete modular plant. A concept for safe and modular interaction of PEAs in a modular plant is developed and additionally validated on a basic demonstrator. Finally, from these findings, standardization requirements are provided and aspects for further research are pointed out.

This paper presents an extension of the previous work on AutomationML-based mappings for semantic interoperability between heterogeneous engineering tools without semantic standardization [1][2]. While the former method is limited to 1:1 semantic mappings, the present paper addresses the more general concept of meaningful interoperability described in [3]. It includes complex semantic transformations and provides means for enabling pragmatic interoperability. An AutomationML-based Reference Class Library is introduced as an evolving knowledge base of interoperability information for a heterogeneous tool landscape. It is accessed via the Interoperability Bridge, a middleware developed for publishing and retrieving this information. The proposed method is applied to a collaborative engineering application for feedback-based improvement of Drive type selections in ABB DriveSize tool. Topics for further extension are also mentioned.

One of the key ideas of Industry 4.0 is the availability of a digital representation of each industrial asset within an Industry 4.0 network, the “Asset Administration Shell” (AAS). The AAS combines data related to an asset and software interfaces which provide access and explorability of the asset data in the network. Because an accepted standard is missing, component manufacturers are currently not able to economically release basic asset models for their products. This creates a deadlock situation for the widespread introduction of Industry 4.0 driven smart factory concepts. This paper describes and compares the most promising approaches to model the asset administration shell: the AAS model of the Platform I4.0, and the AutomationML component description of the AutomationML e.V. The technical concepts are comparatively analyzed for strengths and weaknesses and a useful combination of both model worlds is proposed.

This paper describes a concept for an electronic data exchange of System Control Diagrams (SCDs) from the process engineering to control engineering domain. SCDs are standardized by the Norwegian NORSOK organisation and contain, in contrast to P&IDs, concrete function blocks and their interrelations, a real representation of the actual control application. SCDs are of interdisciplinary nature containing process and automation information at the same diagram. Their application in the Norwegian oil and gas industry is well established and of high value for the automation departments and control room operators. Seamless electronic data exchange is however not established today, the printed diagram is still the main input for the control system supplier's engineers. This paper investigates a way to utilize AutomationML to overcome this issue.

To simplify the engineering of field devices, almost every fieldbus organization has developed its own Device Description Language (DDL), a formal language to describe the services and configuration options of field devices. The DDLs are usually tailored to the needs of the associated fieldbus engineering tool chain and unusable in tool chains of other vendors or in tools for other lifecycle phases. This paper describes a generic approach to overcome these shortcomings by means of AutomationML and provides an example for IO-Link masters and devices. The proposed method is generic and re-usable for other DDLs.

Computer Aided Engineering Exchange (CAEX) is one of the most promising standards when it comes to data exchange between engineering tools in the production system automation domain. This is also reflected by the current emergence of AutomationML (AML), which uses CAEX as its core representation language. However, with the increasing use of CAEX, important language engineering challenges arise. One of these challenges is the customization of CAEX for its usage in superior standards, such as AML, which requires the precise specification of the language including the formalization and validation of additional usage rules. Another highly topical challenge is the ongoing evolution of CAEX as has recently happened with the transition from version 2.15 to version 3.0. Further challenges include the provisioning of editing facilities and visualizations of CAEX documents such that they can be inspected and modified by engineers, and the development of transformations from and to CAEX such that different engineering artifacts can be exchanged via CAEX. In this paper, we take a language engineering point of view and present a model-driven engineering (MDE) workbench for CAEX that allows to address these and other challenges. In particular, we present how CAEX can be formulated in a model-based framework, which allows the application of MDE techniques, such as model validation, migration, editing, visualization, and transformation techniques, to solve a diverse set of language engineering challenges experienced for CAEX. We give an overview of the developed workbench and illustrate its benefits with a focus on customizing CAEX for AML and evolving CAEX documents from version 2.15 to 3.0.

This paper describes a concept and its industrial pilot of a methodology for how to automate the data exchange between multiple engineering tools by means of AutomationML without a predefined semantic harmonization or a central database. Key challenges in the methodology and its industrial application are interoperability between 20 participating independent engineering tool platforms, identification of inconsistencies and guiding the project manager to areas of inconsistency. The key focus of this paper is not a commercial product; instead, the authors describe the methodological challenges, derive related requirements and describe the concepts to fulfill the collected requirements.

Industry 4.0 has come up with an impressive number of additional terms and definitions e.g. Asset Administration Shell or Digital Twin. Those terms stand for Industry 4.0 coreparadigms, but their meaning is not harmonized even among experts. This is a source of misunderstanding and confusion. In this paper, the mentioned terms are discussed along the life cycle of a plant. A plant's life cycle comprises the whole process from its idea to its destruction. During the discussion of the terms not only the concepts, but also the visions of Industry 4.0 are clarified. Goal of this paper is not to define additional terms, but to explain and substantiate existing ones and to solve apparent contradictions. Additional outcome of the discussion are architectural recommendations for an upcoming Industry 4.0 architecture. Moreover, suggestions for device manufacturers, system integrators, plant owners and Industry 4.0 architects are given.

CAEX is one of the most promising standards when it comes to data exchange between engineering tools in the production system automation domain. This is also reflected by the current emergence of AutomationML, which uses CAEX as its core representation data format. Having such standards at hand, the question arises how to deal with the evolution of such standards as is currently happening with the transition from CAEX 2.15 to CAEX 3.0. In this paper, we take a language engineering point of view to the evolution of engineering data formats. In particular, we present how CAEX can be formulated in a model-based framework, which allows to reason about evolution of the data format as well as its impact on the data stored in such evolving formats. By this, not only the migration process of existing data to the new format version is possible, but also a more theoretical investigation on information preservation is possible. We demonstrate the approach by the concrete case of the upcoming CAEX evolution.

This paper describes an innovative approach and infrastructure for a seamless data communication between CAE and automation systems' engineering tools. In this approach syntax, semantics and graphical representations of various CAE tools can be captured, analyzed, visualized and mapped to an intermediate syntax and semantic in a data hub. Different captures from different data sources can be merged together and analyzed in order to enrich the data in the hub. Achieved results can consequently be shaped and mapped to any target syntax and semantics in a sandbox environment in order to be exported from the hub. This approach allows a bidirectional data flow from multiple CAE data sources and automation systems by utilizing a multi-level mapping library. All the intakes and outcomes of the hub are managed by a web based version control system which can provide the delta view between different versions regardless of their origin syntax or semantics. By one pass of data between the source and the target, the persistent bridge in the hub is formed and this bridge will be enriched and enhanced by each further pass of data. A prototypic development have been performed and the complete communication cycle have been verified using a sample P&ID and signal list from various CAE tools and an outcome of AutomationML format compliant to ABB's engineering tool as the automation target system.

Standardization of data models in automation industry is a tedious, time-consuming and multidisciplinary process. The absence of a successfully standardized and established data exchange methodology for PCE (Process Control Equipment) Requests as the most informative capsule of data is an unsolved gap between process and control engineering tools. This contribution describes the first application of a novel and agile standardization method, applied by means of PCE Requests. In the result, within 10 month, leading CAE tool vendors and users of engineering tools have developed a detailed standard electronic data model of the PCE Request, as well as prototype exporter and importer for process and control engineering tools. Consequently, the bidirectional data exchange of PCE Requests between multiple software tools in various disciplines have been successfully demonstrated at the NAMUR Annual General Meeting 2014.

Engineering is a major cost factor in automation system projects. Hence, both industry and academia aim to develop new methods and concepts to increase engineering efficiency. However, the verification and comparison of those methods is difficult because the measurement of engineering efficiency is unsolved and is considered to be a possible breakthrough in the domain of engineering. This paper proposes an approach to measure engineering efficiency in a systematic, reproducible and comparable way. The proposed approach allows a systematic assessment of any new engineering method, tool or concept and hence allows a systematic comparison of alternatives. To evaluate the proposed approach, an automatic measurement tool was developed and applied on a benchmark project. First results are described and discussed.

Interoperability of engineering tools, which is important for engineering efficiency, requires the openness of the tools for the import and/or export of engineering data. A metric to assess the openness of an engineering tool has been presented recently at IEEE ETFA 2012. This contribution reflects the methodology and focuses on the initial empirical assessment of the method with data from industrial practice, i.e., the comparison of openness evaluation of well-established engineering tools. The authors report evaluation results and discuss lessons learnt to guide improving the openness of automation tools for supporting more efficient engineering processes. Major results are: the measurement method has been found useful and usable in practice and pointed out strengths and weaknesses in 15 well-established engineering tools from 4 tool categories.

Interoperability describes the ability of engineering tools to collaborate across tool borders, organizational borders, and workflow phases. Therefore, it is considered as an important indicator for engineering efficiency, but is supported by today's industrial software only to some extent. The authors propose a novel method for the assessment of automation engineering tools, regarding their possibility to provide data for other software tools and to make use of the data provided by other tools. The authors have developed a metric to systematically assess and compare the openness of engineering tools. Thus, users can estimate better and for the first time based on objective criteria whether an engineering tool is suitable for a particular need within an engineering use case, and suppliers might find interesting hints towards possible improvements.

Data exchange between engineering tools has been identified as a major bottleneck in the engineering workflow. AutomationML aims to close this gap by supporting the exchange of a variety of aspects of automation engineering and its ability to manage multiple semantics in a heterogeneous engineering tool landscape. But beside all AutomationML-concepts, the introduction in the industry is bound to its ease of use. This paper gives an overview about the results of an activity of the AutomationML society which investigated concepts to reduce the AutomationML programming effort. After an introduction into basic requirements, this paper proposes a generic design for im-and exporter software and explains how to technically manage semantic variety. In addition, the author gives an overview about typical errors in AutomationML object modeling and how to avoid them. Finally, this paper wraps up with a major prejudice about XML based file exchange, the lack of performance, and presents encouraging results of a CAEX performance investigation.

This contribution discusses the well-known paradox of today's approaches to standardizing of semantic models in automation engineering and proposes an evolutionary concept to resolve it. In this context, an overview of past and current standardization activities is presented which points out the existing barriers. Instead of aiming for a completely standardized neutral data model, the proposed approach actively supports data exchange with mixed neutral and proprietary data models. Hence, it utilizes the existing heterogeneity and the maturity of proprietary data models. In this context, the authors present a concept of maturity levels, which form milestones towards a stepwise evolution of a semantic standardization. Finally, this paper describes how this approach is technically implemented in AutomationML.

This contribution defines for the first time “safety automata”, a specification language for safety control functions. This fills an important gap in the method tool box of the automation engineer. The definition has a profound potential to broaden the use of automata and their well-known advantages in industrial applications. The advantages and application of safety automata are explained by example via the function block SF_Equivalent of the PLCopen specification. Stepwise, the authors illustrate the specification of this function block by means of safety automata, the transformation rules to implement the automata in fully functional PLC code, and finally the generation of test cases that allow checking both the automata and the PLC code.

Interoperability of engineering tools describes their ability to collaborate with each other across their tool borders, company locations and workflow phases. Therefore it is considered as an important indicator for engineering efficiency - but it is rarely supported by today's heterogeneous industrial software. This paper motivates the need of interoperability, describes the state of the art and its issues, derives requirements and presents a new concept to overcome the mentioned issues. This approach introduces the concepts of Collaboration Objects, Data Ownership and Data Exchange Feedback Loops and utilizes a file based data exchange by means of AutomationML. Finally, the paper describes the developed workflow concept together with required software functionality for a collaboration middleware.

AutomationML (Automation Markup Language) is an XML based data format for the exchange of plant engineering information. Its mission is to interconnect engineering tools of different disciplines, e.g. process, mechanical or control engineering. AutomationML enables the vendor independent storage of interlinked engineering data that is usually distributed into different engineering tools. This enables a wide range of future tool functionality not available and not possible today. For this, AutomationML defines a number of extended concepts for different engineering aspects. One of them is the process-resource-product concept which was introduced with version 1.1 and published at the Hannover trade fair 2009. This paper explains this concept by means of an industrial example.

This contribution presents the basic architecture of the neutral data format AutomationML developed by the companies Daimler, ABB, Siemens, Rockwell, Kuka, Zuhlke, netAllied and the universities of Magdeburg and Karlsruhe. AutomationML serves for the data exchange between manufacturing engineering tools and therefore supports the interoperability between them. It covers information about the plant structure (topology and geometry) and the plant behaviour (logic and kinematics). The first version of AutomationML has been presented at the Hannover fair in 2008.

This contribution investigates the topic ldquovirtual commissioningrdquo from an industrial point of view and proposes an evolutionary and realistic technical concept for the introduction of virtual commissioning environments. Main points of this paper are the identification and formulation of industrial requirements for the introduction of a virtual commissioning environment, a proof of commercial tools against these requirements and a proposal of an evolutionary technical concept. Finally, this paper reports first experiences about a prototypical implementation of the proposed approach.

Reports about experiences while designing and implementing a Java based industrial software which enables the (semi)-automatically translation of control programs from one platform to another one. Java had been chosen as the implementation language for this project with respect to the advantages of the object oriented concepts within Java, the reduced implementation effort, the well supported distributed development and the desired software stability. The resulting software framework, designed and implemented by scientists and developer groups in different countries, has already been employed successfully; the project can be regarded as a distinctive success. The paper focuses on the software design, on the implementation experiences, and especially on the crucial choice of the implementation language and environment, which accounts for a significant part of the project's success.