AutomationML Research Articles

Methods to Enable Evolvable Digital Twins for Flexible Automated Production Systems

Changing requirements cause flexible automated Production Systems (aPS) to evolve over decades. Digital Twins (DT) of the different hierarchy levels and design steps ease this evolution, e.g., by enabling requirement analysis and compatibility checks ahead of any physical changes. To ensure up-to-date models and integrate additional knowledge, information gained during operation is included in DTs. Consequently, evolvability, decomposability, control software modularity, and learning during operation are identified as four requirements to achieve such evolvable DTs. Concepts to realize every requirement are introduced and exemplarily validated using a demonstrator machine. AutomationML (AML), the XML-based vendor neutral language for information modeling and exchange in between different disciplines and their tools and product classification systems like ECLASS that specify components attributes vendor neutral enable evolvability during the design phase. Decomposability is achieved by assembling DTs of components according to ISA 88 levels from control unit to facility. A control primitive concept that realizes control software modularity is introduced and validated. Based on data analytics and operation data the DT can be updated by using the versioning mechanism of AML. Thereby, the DT for the next machine generation is improved with knowledge from operation and represents the already existing machine more precisely.

Requirements and software engineering for automotive perception systems: an interview study

Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and—most importantly—safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems.

A workflow towards a strongly typed AutomationML API

Abstract Over the lifecycle of a production plant digitalization leads to a large data footprint along different software (SW) components. A digital data exchange is the fundament to support data integrity, prevent data loss and avoid duplicate work. AutomationML (AML) is a commonly accepted tool-independent format to exchange data of different domains along the lifecycle of a production plant. This contribution provides an easy-to-use workflow that empowers SW components to transform AML into a strongly typed class hierarchy which is the basis for an efficient and maintainable SW solution.

Architecture design and prototype system development: Blockchain-driven D&A Interoperability implementation based on AAS-Work-Centre

DT and AAS are two important technical means to connect the manufacturing physical world and information space. Based on the technical vision of realizing D&A (DT&AAS) automatic configuration, standard implementation and trusted interoperability process, this paper takes AAS as the work center, and integrates and designs a blockchain-driven D&A interoperability implementation architecture in the I4.0 workshop. (1) D&A interoperability dimension and function design adopts the unified standard framework RAMI4.0; (2) D&A information architecture design introduces the open-source BC the Tangle cross-chain interoperability framework; (3) D&A data interaction design based on Automation ML server framework; (4) The I4.0 workshop implementation framework takes AAS as the work center. Based on the above conceptual framework, an experimental case is designed and developed.

A Microservice- and AutomationML-based Reference Architecture for an Engineering Configurator Web Platform

Robot-centric automation solutions (RAS) promise more efficiency and quality in production as well as more economic stability and security through local production, especially in the times of COVID-19. Nevertheless, due to their high complexity and costs, RAS are only used to a limited extent by small and medium-sized manufacturing companies. As a rule, the high costs of RAS arise from individual engineering, which takes up to 70 percent of the acquisition costs. For this reason, it is necessary to optimise the engineering of RAS. Among other things, better software and less costly top-down engineering methods are needed for this kind of enhancement. Pure optimisation via more advanced simulation tools is not sufficient. Configurators in particular offer the possibility of automatically arranging individual solutions from existing experience. Up to now, however, configurators have been used primarily for the individualisation of products, such as automobiles or clothing, on the basis of the variants predefined by the manufacturer, and less for the engineering of automation solutions. To make top-down configuration and simulation functions easily available to small medium enterprises, a web platform is a convenient solution. Due to the high complexity within the engineering domain, it is reasonable to build this kind of web platform with different services from specialised companies, e.g. by using microservices (MS). Conceivable roles could be as following. The web platform operator (WPO) offers an industry-specific, turnkey complete web platform. The WPO combines, integrates and orchestrates various modules and MS like configuration, simulation, data exchange and other. The MS can be provided by MS providers. In particular, since data interoperability and exchange are very important for engineering, an AutomationML (AML) MS is used to implement both. AML particularly supports the generic exchange of data based on industrial standards. To provide a general solution, a MS and AML-based web platform reference architecture was developed. These concepts and MS architecture are validated via a prototypical implementation and utilisation of the web platform within the scope of the ROBOTOP research project. Thus, the potential for increasing efficiency in engineering for RAS could be demonstrated using this top-down approach.

Automated Security Risk Identification Using AutomationML-Based Engineering Data

Systems integrators and vendors of industrial components need to establish a security-by-design approach, which includes the assessment and subsequent treatment of security risks. However, conducting security risk assessments along the engineering process is a costly and labor-intensive endeavor due to the complexity of the system(s) under consideration and the lack of automated methods. This, in turn, hampers the ability of security analysts to assess risks pertaining to cyber-physical systems (CPSs) in an efficient manner. In this work, we propose a method that automatically identifies security risks based on the CPS's data representation, which exists within engineering artifacts. To lay the foundation for our method, we present security-focused semantics for the engineering data exchange format AutomationML (AML). These semantics enable the reuse of security-relevant know-how in AML artifacts by means of a formal knowledge representation, modeled with a security-enriched ontology. Our method is capable of automating the identification of security risk sources and potential consequences in order to construct cyber-physical attack graphs that capture the paths adversaries may take. We demonstrate the benefits of the proposed method through a case study and an open-source prototypical implementation. Finally, we prove that our solution is scalable by conducting a rigorous performance evaluation.

Data Exchange of Digital Twins Based on AML in Space Science Experiment Equipment

With the development of Computer Aided Design(CAD) and manufacturing technology, various industries are transforming to digitalization, and the realization of the digital model of aerospace products is an important part of “digital aerospace”. Based on the theory of system engineering, this paper proposes a three-dimensional conceptual model of analysis of payload in the life-cycle of physical device design, as well as the concept of digital twin five-dimensional model in the design stage. Combined with the actual application requirements, this paper explores the content and standards of digital twin model construction of payloads, by using the model mapped to the AutomationML(AML) to realize the data exchange and fusion.

Automatic simulation model implementation of robotic production cells in a 3D manufacturing simulation environment

Abstract Due to the high demand placed on today’s production systems, such as high flexibility or low cost and time requirements, simulations tools continue to gain more importance. With the help of simulation tools, complex production systems can be designed as well as examined in terms of feasibility before their actual physical existence in order to make them controllable and to avoid possible cost-intensive errors. However, creating and implementing a simulation model manually takes a considerable amount of time and expert knowledge regarding the used simulation tool. A promising approach for this is to automate the implementation of simulation models using existing data about product, processes and resources (PPR-Triple). Therefore, the simulation model should represent the layout as well as the executed processes and workflows. For this purpose, the presented paper focuses on the automatic workflow and process transfer to the 3D simulation environment of Visual Components. For the representation of the imported layout and process data AutomationML (AML) is extended and utilized as a data exchange format.

Integrated design environment for reusable modular assembly systems

PurposeThis paper aims to provide a service-based integrated prototype framework for the design of reusable modular assembly systems (RMAS) incorporating reusability of equipment into the process. It extends AutomationML (AML) developments for an engineering data exchange to integrate and standardize the data formats that support the design of RMAS.Design/methodology/approachThe approach provides a set of systematic procedures and support tools for the design of RMAS. This includes enhanced domain knowledge models that facilitate the interpretation and integration of information across the design phases.FindingsThe inclusion of reusability aspects in the design phase improves the sustainability of future assembly systems, by ensuring equipment use until its end-of-life. Moreover, the integrated support tools reduce the design time, while improving the quality/performance of the system design solution, as it enables the exploration of a larger solution space. This will result in a better response to dynamic and rapidly changing system requirements.Social implicationsThis work provides a sustainable approach for the design of modular assembly systems (MAS), which will ensure better resource utilization. Additionally, the standardization of the data and the support of low cost tools is expected to benefit industrial companies, particularly the small- and medium-sized enterprises.Originality/valueThis approach offers a service-based platform which uses production data to incorporate reusability aspects into the design process of modular assembly system. Moreover, it provides a framework for modular assembly system design by extending the current design processes and interactions between stakeholders. To support this, a standardized method for information representation and exchange across the several phases of the RMAS design activity is briefly illustrated with an industrial case study.

An AutomationML Based Ontology for Sensor Fusion in Industrial Plants.

AutomationML (AML) can be seen as a partial knowledge-based solution for manufacturing and automation domains since it permits integrating different engineering data format, and also contains information about physical and logical structures of production systems, using basic concepts as resources, process, and products, in semantic structures. However, it is not a complete knowledge-based solution because it does not have mechanisms for querying and reasoning procedures, which are basic functions for semantic inferences. Additionally, AutomationML does not deal with aspects of sensor fusion naturally. In this sense, we propose an ontology to describe those sensors’ fusion elements, including procedures for runtime processing, and also elements that can turn AutomationML into a complete knowledge-based solution. The approach was applied in a case study with two different industrial processes with some sensors under fusion. The results obtained demonstrate that the ontology allows describing sensors that are under fusion and deal with the occurrence of data divergence. In a broader view, the results show how to apply AutomationML description for runtime processing of data generated from different sensors of a manufacturing system using an ontology to complement the AML description, where AutomationML concentrates knowledge about a specific production system and the ontology describes a general and reusable knowledge about sensor fusion.

Model-based generation of run-time data collection systems exploiting AutomationML

Abstract Production system operators need support for collecting and pre-processing data on production systems consisting of several system components, as foundation for optimization and defect detection. Traditional approaches based on hard-coded programming of such run-time data collection systems take time and effort, and require both domain and technology knowledge. In this article, we introduce the AML-RTDC approach, which combines the strengths of AutomationML (AML) data modeling and model-driven engineering, to reduce the manual effort for realizing the run-time data collection (RTDC) system. We evaluate the feasibility of the AML-RTDC approach with a demonstration case about a lab-sized production system and a use case based on real-world requirements.

A Model-Driven Engineering Workbench for CAEX Supporting Language Customization and Evolution

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.

Generation of Simulation Models in MATLAB-Simulink Based on AutomationML Plant Description

Process simulations are useful test-beds for experiments and optimizations along the entire industrial plant life-cycle. Shifting testing and tuning of industrial plants and their automation systems from the real world to simulated environments is a part of a virtualization, which is one of the key movements in emerging areas of Industry 4.0 and factories of the future. Although simulations bring a large variety of benefits, they suffer from a time-consuming and error-prone design phase, which limits their use in industrial practice. This paper proposes a new design method called AML2SIM, which transforms the real plant description represented in AutomationML (AML) and generates a dynamic simulation model (SIM). The proposed method signifcantly improves the engineering and re-design of simulation models in terms of saving time and effort of experts as the models can be easily re-generated based on a given AutomationML plant model. Simulations are assembled from simulation blocks that are shared among various projects in simulation libraries, hence the method contributes to reuse of simulation artifacts.