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Efficient Engineering Data Exchange in Multi-disciplinary Systems Engineering

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Abstract

In the parallel engineering of industrial production systems, domain experts from several disciplines need to exchange data efficiently to prevent the divergence of local engineering models. However, the data synchronization is hard (a) as it may be unclear what data consumers need and (b) due to the heterogeneity of local engineering artifacts. In this paper, we introduce use cases and a process for efficient Engineering Data Exchange (EDEx) that guides the definition and semantic mapping of data elements for exchange and facilitates the frequent synchronization between domain experts. We identify main elements of an EDEx information system to automate the EDEx process. We evaluate the effectiveness and effort of the EDEx process and concepts in a feasibility case study with requirements and data from real-world use cases at a large production system engineering company. The domain experts found the EDEx process more effective and the EDEx operation more efficient than the traditional point-to-point process, and providing insight for advanced analyses.

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... Figure 3 shows the representation of tree structure of the AMLEditor (1). On the right (2), shows the SystemUnitClass libraries, bottom left RoleClass libararies (3) and bottom right InterfaceClass libraries (4). Individual nodes can be inspected to view sub-nodes and the side panel helps to understand the project instance hierarchy (5). ...
... Traditionally, model engineering in PSE has mainly focused on paper plans and slowly has transformed to the digital era over the last decades. However, discipline-specific views have prevailed and the integration of engineering data is still an open challenge in every complex PSE project [4]. Figure 4 presents an approach for multi-model unification into a common view and thus, identifying common concepts for holistic system understanding and modeling [29]. ...
... On the top right of the window, detailed information is shown, such as the name, id or parent node (3). Different discipline-specific views are shown in (4). Add, remove and edit operations are supported (5). ...
Chapter
Background. In Production Systems Engineering (PSE) models, which describe plants, represent different views on several engineering disciplines (such as mechanical, electrical and software engineering) and may contain up to 10,000s of instance elements, such as concepts, attributes and relationships. Validating these models requires an integrated multi-model view and the domain expertise of human experts related to individual views. Unfortunately, the heterogeneity of disciplines, tools, and data formats makes it hard to provide a technology-independent multi-model view. Aim. In this paper, we aim at improving Multi-Model Reviewing (MMR) capabilities of domain experts based on selected model visualisation methods and mechanisms. Method. We (a) derive requirements for graph-based visualisation to facilitate reviewing multi-disciplinary models; (b) introduce the MMR approach to visualise engineering models for review as hierarchical and linked structures; (c) design an MMR software prototype; and (d) evaluate the prototype based on tasks derived from real-world PSE use cases. For evaluation purposes we compare capabilities of the MMR prototype and a text-based model editor. Results. The MMR prototype enabled performing the evaluation tasks in most cases considerable faster than the standard text-based model editor. Conclusion. The promising results of the MMR approach in the evaluation context warrant empirical studies with a wider range of domain experts and use cases on the usability and usefulness of the MMR approach in practice.
... Software supports the achievement of production goals of a working cell in production systems in terms of speed or output by controlling robotic arms, pumps or transportation systems in an efficient way. However, in traditional industrial projects SE contributions are made in the final phases of the design phase based on the engineering information derived from other disciplines and thus, have to rely on the quality and availability of exchanged data between engineering disciplines, such as mechanical, electrical or pneumatics [6]. ...
... Additional information is provided through unstructured communication channels, face-to-face or via telephone. A more detailed explanation of the simulation engineer and project manager use case is provided in [6]. ...
... Martini et al. have shown that approximately a quarter of time is spent for repayment and management of accumulated TD [21]. Various types of debt [6] have been detected, as Li et al. collect ten types, ranging from test to documentation and infrastructure debt [19]. ...
Conference Paper
Technical Debt (TD) has proven to be a suitable communication concept for software-intensive contexts to raise awareness regarding longterm negative effects of deviations from standards and guidelines. TD has also been introduced to systems engineering domain, to communicate design shortcomings in long-running, software-assisted systems. We analysed potential TD in the engineering data exchange for production system engineering. Similar to requirements engineering in software-intensive systems, data exchange in the design phase plays an integral part in Software Engineering (SE) for Production Systems Engineering: Specifications, and physical logic have to be derived from heterogeneous plant models or parameter tables designed by different stakeholders. However, traditional procedures and inadequate tool support lead to inefficient data extraction and integration. We identified debt arising from knowledge representation, data model and the exchange process. The refinement validation of identified TD was achieved through semi-structured interviews with representatives in two analysed companies. In an online survey with ten participants from an industrial consortium we evaluated whether the identified TD concepts also applied to other companies, which is true for the majority of TD. Furthermore, we discuss promising TD management strategies to repay and manage negative effects and the accumulation of additional debt, such as improved communication, test-driven model engineering and visualisation of engineering models.
... Ein manueller Datenabgleich oder der Einsatz von Behelfslösungen (etwa spezifische Datenbank-oder Software-Lösungen, die durch lokale Experten als individuelle Unterstützung für den Datenaustausch erstellt und verwendet werden) ist jedoch meist zeitaufwändig, fehleranfällig und birgt mitunter ein hohes Risiko für den Projekterfolg. Integrations-oder Datenaustauschplattformen (Biffl et al. 2019a(Biffl et al. , 2019b können einen effizienten Datenaustausch zwischen heterogenen Fachbereichen ermöglichen. Zur Qualitätsverbesserung können Maßnahmen der Qualitätssicherung helfen, Fehler -speziell in fachbereichsübergreifenden Datenmodellen -frühzeitig zu finden und dadurch das Risiko für den Projekterfolg zu minimieren (Biffl et al. 2011(Biffl et al. , 2019a (Winkler et al. 2017c). ...
... Dadurch ergibt sich typischerweise neben einer effizienteren und zielgerichteten Fehlersuche auch eine massive Reduktion von Aufwand und Kosten bei der Fehlersuche (Winkler & Biffl 2015) (Winkler et al. 2016). Der Einsatz von Inspektionen in einer Datenlogistik (Biffl et al. 2019b) erlaubt es zusätzlich, auch fehlende Informationen aufzudecken, etwa fehlende PLC-Komponenten oder elektrische Komponenten, die zwar in der Anlagentopologie verfügbar sind, aber noch nicht umgesetzt sind. Durch die Verfügbarkeit von Verknüpfungen, die über die Datenlogistik hergestellt werden können (Biffl et al. 2015(Biffl et al. , 2019b, ist im Bedarfsfall auch eine Navigation nicht nur zu den betroffenen Modellen, sondern auch zu den relevanten Positionen innerhalb dieser Modelle möglich (Mordinyi et al. 2012). ...
... Der Einsatz von Inspektionen in einer Datenlogistik (Biffl et al. 2019b) erlaubt es zusätzlich, auch fehlende Informationen aufzudecken, etwa fehlende PLC-Komponenten oder elektrische Komponenten, die zwar in der Anlagentopologie verfügbar sind, aber noch nicht umgesetzt sind. Durch die Verfügbarkeit von Verknüpfungen, die über die Datenlogistik hergestellt werden können (Biffl et al. 2015(Biffl et al. , 2019b, ist im Bedarfsfall auch eine Navigation nicht nur zu den betroffenen Modellen, sondern auch zu den relevanten Positionen innerhalb dieser Modelle möglich (Mordinyi et al. 2012). ...
... In a typical PSE process, the domain experts work in parallel in discipline-specific workgroups that exchange engineering artifacts for iterative improvement. For making informed design decisions, industrial automation and software engineers depend on the high quality of input artifacts that contain software requirements as well as results and rationale of system design decisions [4] [5]. ...
... Martini et al. [18] show how architectural TD accumulates during development in a project until reaching a crisis point that makes refactoring inevitable, increasing business value as the short-term sins are repaid adequately. Case studies by Biffl et al. [4][5] and Kathrein et al. [12][13] investigated engineering processes of EOs with a focus on the structure of collaborations between workgroups [13] and how data is exchanged [4] [5]. These works represent building blocks for this paper, as they define a coherent context with basic concepts needed for TD investigations. ...
... The production optimizer (PO) receives all basic plans and tries to minimize the cycle times of the plant. However, this work requires different product/ion knowledge aspects and may cause many calls back to the SP and PP (5). The PO collaborates with the automation engineer (AE) (6), who is responsible for PLC software engineering tasks. ...
... Damit lassen sich Errichtungs-und Stillstandzeiten, beispielsweise bei Änderungen oder Wartungsaufgaben, signifikant reduzieren. Durch eine virtuelle Inbetriebnahme können auch Fehler frühzeitig erkannt und somit die Fehlerrate reduziert werden (Biffl et al. 2019e) (Pöschl et al. 2018) (Wünsch 2008). Diesen Vorteilen stehen aber erhebliche Aufwände für die Erstellung von Simulationen gegenüber. ...
... Das Hauptziel von EDaLIS ist, gemeinsame Konzepte zwischen verschiedenen fachbereichsspezifischen Sichten zu pflegen und zu visualisieren. Um diese gemeinsamen Konzepte zu erreichen, wird ein zweistufiger Prozess durchlaufen (Biffl et al. 2019e ...
... Further, several approaches enable the mapping of engineering models based on common data models [21], [27]. Biffl et al. suggest an engineering data logistics approach, to increase the quality of exchanged engineering data [3] [5]. Finally, there are data management systems, such as engineering buses, acting as engineering backbone providing most advanced data integration and exchange capabilities. ...
... In the case study, a typical use case concerned the CPPSE data exchange from several providers to a consumer, such as an automation engineer. Usually, data-exchangerelated actions of data consumers cover (1a) one-time data exchange from a source, (1b) merging data from several sources, and (2) multi-time data exchange from a source, see a more detailed use case description online 1 [3], [4] . ...
... Information exchange among disciplines and tools is another application scenario that aims to improve the efficiency and effectiveness of interdisciplinary collaboration [7,11,43]. A variety of exchange formats exist in order to exchange information between different disciplines and tools. ...
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The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: “SysML – AutomationML” and “SysML4Mechatronics – AutomationML.” Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems.
... The technical results indicate coordination based on detailed asset property dependencies to be feasible for change validation, assuming data sources on a suitable level of detail and accessible with efficient approaches, e.g., data logistics [42]. ...
... In [14], the authors describe an approach for efficient data exchange in multi-disciplinary engineering environments, suggesting a two-phase data exchange process architecture. In the data definition & negotiation phase, the role of a data curator is introduced, a domain expert who has a relevant background domain knowledge to mediate between workgroups. ...
Conference Paper
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The Internet of Things and Services opens new perspectives for goods and value-added services in various industrial sectors. Engineering of industrial products and of industrial production systems is a multi-disciplinary, model- and data-driven engineering process, which involves engineers coming from several engineering disciplines. These engineering disciplines exploit a variety of engineering tools and information processing systems. This book discusses challenges and solutions for the required information processing and management capabilities within the context of multi-disciplinary engineering of production systems. The authors consider methods, architectures, and technologies applicable in use cases according to the viewpoints of product engineering and production system engineering, and regarding the triangle of (1) the product to be produced by (2) a production process executed on (3) a production system resource. This chapter motivates the need for better approaches to multi-disciplinary engineering (MDE) for cyber-physical production systems (CPPS) and provides background information for non-experts to explain the interaction between production engineering, production systems engineering, and enabling contributions from informatics. Furthermore, the chapter introduces a set of research questions and provides an overview on the book structure, chapter contributions, and benefits to the target audiences.
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This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away. The book is organized into two main parts. The first part discusses the foundations of MDSE in terms of basic concepts (i.e., models and transformations), driving principles, a...
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An important element of implementing a data integration solution in multi-disciplinary engineering settings, consists in identifying and defining relations between the different engineering data models and data sets that need to be integrated. The ontology matching field investigates methods and tools for discovering relations between semantic data sources and representing them. In this chapter, we look at ontology matching issues in the context of integrating engineering knowledge. We first discuss what types of relations typically occur between engineering objects in multi-disciplinary engineering environments taking a use case in the power plant engineering domain as a running example. We then overview available technologies for mappings definition between ontologies, focusing on those currently most widely used in practice and briefly discuss their capabilities for mapping representation and potential processing. Finally, we illustrate how mappings in the sample project in power plant engineering domain can be generated from the definitions in the Expressive and Declarative Ontology Alignment Language (EDOAL).
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This book provides guidelines for practicing design science in the fields of information systems and software engineering research. A design process usually iterates over two activities: first designing an artifact that improves something for stakeholders and subsequently empirically investigating the performance of that artifact in its context. This validation in context is a key feature of the book - since an artifact is designed for a context, it should also be validated in this context.
Data interface for coil car simulation (case study) part II - detailed data and process models
  • S Biffl
  • M Eckhart
  • A Lüder
  • T Müller
  • F Rinker
  • D Winkler
Stefan Biffl, Matthias Eckhart, Arndt Lüder, Thorsten Müller, Felix Rinker, and Dietmar Winkler. Data Interface for Coil Car Simulation (Case Study) Part I. Tech. Report CDL-SQI-M2-TR02, TU Wien, 2018.
Introducing engineering data logistics for production systems engineering
  • S Biffl
  • A Lüder
  • F Rinker
  • L Waltersdorfer
  • D Winkler
Stefan Biffl, Arndt Lüder, Felix Rinker, Laura Waltersdorfer, and Dietmar Winkler. Introducing engineering data logistics for production systems engineering. Tech. Report CDL-SQI-2018-10, TU Wien, http://qse.ifs.tuwien.ac.at/wpcontent/uploads/CDL-SQI-2018-10.pdf, 2018.
Migration to AutomationML based tool chains - incrementally overcoming engineering network challenges
  • A Lüder
  • J.-L Pauly
  • K Kirchheim
  • F Rinker
  • S Biffl
Arndt Lüder, Johanna-Lisa Pauly, Konstantin Kirchheim, Felix Rinker, and Stefan Biffl. Migration to AutomationML based Tool Chains -incrementally overcoming Engineering Network Challenges. In 5th AutomationML User Conference, 2018.