A Holistic System Lifecycle Engineering Approach – Closing the Loop between System Architecture and Digital Twins

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29th CIRP Design 2019 (CIRP Design 2019) 29th CIRP Design 2019 (CIRP Design 2019)

A Lifecycle Engineering Approach – A Holistic Holistic System System Lifecycle Engineering Approach – Closing Closing the the Loop Loop 28th CIRP Design Conference, May 2018, Nantes, France between System Architecture and Digital Twins between System Architecture and Digital Twins A new methodology to analyze the functional and physical architecture of Dickopf, Thomasbb; Apostolov, Hristoaa*; Müller, Patrickbb; Göbel, Jens C.aa; Forte, Svenaa Dickopf, Thomas ; for Apostolov, Hristo *; Müller, Patrick ; Göbel, Jens C.identification ; Forte, Sven existing products an assembly oriented product family

Institute of Virtual Product Engineering - VPE, University of Kaiserslautern, Germany b Engineering - VPE, University of Kaiserslautern, Germany Institute of Virtual Product CONTACT Software Ltd., Germany b CONTACT Software Ltd., Germany * Corresponding author. Tel.: +49-631-205-3787; fax: +49-631-205- 3872. E-mail address: [email protected] * Corresponding author. Tel.: +49-631-205-3787; fax: +49-631-205- 3872. E-mail address: [email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France a a

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

*Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]

Abstract

This contribution introduces an approach for the optimization of smart products and systems in the early development phases through a ClosedThis introducesapproach. an approach the optimization and systems in the early and development phases through a ClosedLoopcontribution Systems Engineering Thisfor approach – holistic of in smart terms products of considering the overall system its usage context, interdisciplinary, Abstract Loop Systemsmultiple Engineering approach. holistic in terms of the overall systemaspects and itsfrom usageboth context, interdisciplinary, overreaching lifecycle phases,This andapproach supported–methodologically asconsidering well as tool-wise – combines Model-Based Systems overreaching multiple lifecycle phases,Lifecycle and supported methodologically as the wellsystem as tool-wise – combines from and bothvalidation Model-Based Systems Engineering and Product resp. System Management to optimize by using advancedaspects verification methods and InEngineering today’s business environment, the trend towards more product variety and customization isadvanced unbroken.verification Due to thisand development, the needand of and Product resp. System Lifecycle Management to optimize the system by using validation methods techniques, in particular Model-, Twin- and System-in-the-Loop, and seamless feedback of product usage data to the early development phase. agile and reconfigurable production systems emerged to cope withand various products and of product families. To design and optimize production techniques, in particular Model-, Twinand System-in-the-Loop, seamless feedback product usage data to the early development phase. This approach has been prototypically applied at the example of a test bed of an autonomous construction area system of systems. systems as wellhas as been to choose the optimal product matches, analysis methods are needed. Indeed,area most of theofknown methods aim to This approach prototypically applied at the exampleproduct of a test bed of an autonomous construction system systems. analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and © 2019 The Authors. Published by Elsevier B.V. © 2019 2019 The Authors. Published Elsevier nature ofThe components. This fact by impedes anB.V. efficient comparison and choice of appropriate product family combinations for the production © Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of of the CIRP Design Conference Conference 2019. 2019 Peer-review under responsibility of the scientific committee the CIRP CIRP Design system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster Peer-review under responsibility of the scientific committee of the Design Conference 2019 these products in new assembly product familiesSystems for theEngineering; optimization of existing assembly lines and the creation Keywords: Model-Based Systems oriented Engineering; Closed-Loop System Lifecycle Management; Internet of Things.of future reconfigurable assembly systems. BasedSystems on Datum Flow Chain, the physical structure of the products is analyzed. Functional are identified, and Keywords: Model-Based Engineering; Closed-Loop Systems Engineering; System Lifecycle Management; Internetsubassemblies of Things. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. Ansoftware-supported industrial case study on two product families of steering columns of 1. Introduction System Lifecycle Management approach 1. Introduction software-supported System Lifecycle Management approach thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. [2]. PLM becomes connected with product instances in the © 2017 The Authors. Published by Elsevier B.V. [2]. becomes ofconnected with product in and the The demand for innovative products from customers and the fieldPLM by application Internet-of-Things (IoT)instances approaches Peer-review underfor responsibility the scientific the the 28th CIRP Design Conferenceof 2018. The demand innovativeofproducts fromcommittee customersofand field by application Internet-of-Things (IoT) approaches and

increasing global completion put pressure on companies and digital twin implementations [30,31]. Furthermore, increasing global completion putinpressure on companies and digital twinof product implementations [30,31]. [3,4] Furthermore, result in a fast development pace the fields of electronics and virtualization validation processes is a key Keywords: Assembly; Design method; Family identification result in a fast development pace in the fields of electronics and virtualization of product validation processes [3,4] a key software. Accordingly, current product innovation is mainly capability of successful engineering enterprises [1]. isClosedsoftware. Accordingly, current product innovation is mainly capability of successful engineering enterprises [1]. Closedbased on the digitization of products and concomitant digital loop Systems Engineering now becomes reality by the based oncreating the digitization products and and concomitant loop Systems now becomes reality the services so-calledofsmart products services. digital These integration of Engineering virtual validation techniques andby field services creating so-called smart products and services. These integration of virtual validation techniques and field 1.technological Introduction advancements contribute to the vision of of the product range and characteristics manufactured and/or connectivity. technological advancements contribute the vision of connectivity. assembled in this system.question In this context, the main challengecan in autonomous, open systems of systems. Thistogrowth, however, One central research is how system validation autonomous, open systems of systems. This growth, however, central question is how system validation can Due atto thethe fast in the domain of modelling andonresearch analysis is now not only to cope with single comes prize of development increased engineering complexity. be One performed an abstract, functional system level (a) before comes at to thea survey prize of the increased engineering complexity. be performed on anare abstract, functional system level (a) before communication and an ongoing trend of digitization and products, a limited product range existing product families, According by Aberdeen Group, complexity was solution elements provided inorform of physical behavior According to a survey by the Aberdeen Group, complexity was solution elements are provided in form of physical behavior digitalization, manufacturing are facing important but also to able todesigns analyze(MCAD, and to compare define the top systems engineering enterprises challenge already in 2014, with models or be detailed ECAD, products software)toand (b) the engineering already in 2014, with models or detailed andinto (b) challenges in today’s market environments: a increase continuing new product families. It can(MCAD, be observed that software) classical existing 51%top of systems respondents ranking itchallenge accordingly and an of taking field data designs collected in a ECAD, standardized way 51% of respondents ranking it accordingly and an increase of taking field data collected in a standardized way into tendency product families regrouped in function 24% overtowards just fourreduction years [1].of product development times and consideration forare these validation tasks. of clients or features. 24% overproduct just fourlifecycles. years [1]. consideration for these validation tasks. shortened In addition, there isof anresearchers increasing However, assembly oriented product families are hardly to find. Systems Engineering attracts the attention In the following sections, a system validation approach for Systems Engineering attracts the attention of researchers In the following sections, a system validation approach for demand of customization, at digitalization the same timeofintraditional a global On the of product family level, differ mainly in two investigating approaches tobeing master Systems Systems (SoS) [5]products that connects SysML-based investigating approaches to master digitalization of traditional Systems of Systems (SoS) [5] that connects SysML-based competition competitors over products, the world.services This trend, main characteristics: (i) the Language) number of simulations componentswith and (ii) the products andwith services towardsallsmart and (OMG Systems Modeling digital products services towards smart from products, services and (OMG Systems Modeling Language) simulations with digital which inducing the development macro to micro type of components (e.g. mechanical, electronical). systemsis inand digitally supported business models. Model-Based product twins and finally with fieldelectrical, data is presented. The systems digitally supported business product twins and with field data is single presented. The markets, results in diminished lot sizes due toModel-Based augmenting Classical methodologies considering mainly products Systems in Engineering (MBSE) and models. Product Lifecycle approach covers thefinally model-based management of the digital Systems Engineering (MBSE) and Product Lifecycle approach covers the model-based management of the digital product varieties(PLM) (high-volume to low-volume production) [1]. or solitary, already families analyze Itthe Management currently merge towards holistic, twins and controls theexisting field dataproduct inputs into the simulation. is Management (PLM) currently merge towards twins and controlsonthe field datalevel inputs into the simulation. It is To cope with this augmenting variety as well as to beholistic, able to product structure a physical (components level) which identify in the existing causes difficulties regarding an efficient definition and 2212-8271 possible © 2019 The optimization Authors. Publishedpotentials by Elsevier B.V. 2212-8271 2019responsibility The it Authors. Published Elsevier B.V.of the Peer-review©under of the scientific committee CIRP Design Conference 2019 of different product families. Addressing this production system, is important tobyhave a precise knowledge comparison Peer-review under responsibility of the scientific committee of the CIRP Design Conference 2019

2212-8271©©2017 2019The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP CIRP Design Conference 2019. Peer-review under responsibility of the committee of the of 28th Design Conference 2018. 10.1016/j.procir.2019.04.257

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a generic approach part of the validation capacities of the VPE SystemDevelopmentMethodology [33,34] relying on a consistent micro-level system architecture, modeled in accordance to the VPEmecPro²methodology [28] for example, and on capable IT tools for system architecting, system simulation and Digital Twin management integratable through standard protocols such as HTTP and MQTT. The approach has been evaluated in a test laboratory environment that uses a smart excavator as part of an autonomous construction area SoS at the University of Kaiserslautern [6] and PLM technology by CONTACT Software, namely CONTACT Elements [7]. 2. State of the Art 2.1. Smart Products Smart Products are discussed as products of cyber-physical nature with a certain degree of autonomy and the capability of peer-to-peer and peer-to-user communication and interaction [8–12]. Such products are meant for integration into different smart environments [12] in order to bring value to the customer and to the provider. Those environments represent systems that consist of smart products, connecting infrastructure, digitally supported services and further elements. Such systems of smart products are disruptively changing nearly every aspect of businesses. The opportunities for new functionalities, better utilization and higher reliability [9] that such product systems allow for drastically increase the complexity enterprises have to deal with, both internally and externally. Externally, cooperation models have to be rethought in order allow companies of different specialization and size to cooperate on equal basis, so that ultimately the vison of autonomous systemof-systems can become reality. Internally, traditional companies have to rethink and reorganize their operational processes, methods and tools in order to make the increased complexity – especially in product development and usage lifecycle phases – more manageable.

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Lifecycle Management was established approximately five years ago as a concept for the management of product- resp. system-related data throughout the lifecycles of contemporary smart products and the system they are integrated in [13,14] (Fig. 1). With its roots in Product Lifecycle Management (PLM), System Lifecycle Management is the general information management concept extending PLM upstream, to the very early, interdisciplinary product development phase, where model-based approaches (cf. MBSE) [2,15] are becoming prevalent nowadays, as well as downstream, to the product usage and support phase [13,14], where product reconfiguration is gaining in importance currently [16]. It integrates information from different sources and of different stakeholder such as system operators, service providers, product manufacturers, etc., throughout the lifecycle of the systems. With respect to the smart product development phase, System Lifecycle Management provides the necessary processes, mechanisms and infrastructure for an efficient management of the information contained in interdisciplinary system models, based on which the system analysis, synthesis and specification take place. In the smart product usage phase, System Lifecycle Management supports the management of instance-specific data (e.g. current product configuration and status, sensor and usage data) and its aggregation, analysis and visualization within an IoT module for example. In combination with traditional PLM capacities, those new capabilities make information feedback form the product usage phase back to the system development phase possible and efficiently practicable. Furthermore, reconfiguration for optimization of the product’s performance based on the actual usage profile of the particular smart product instance can be implemented. All these aspects contribute to achieve a holistic view of the system of integrated smart product systems and services throughout the entire lifecycle, and increases significantly the potential benefits from data collection and analysis for the involved parties. 2.3. Closed-loop Systems Engineering

Fig. 1: System of system at the example of an autonomous construction area with focus on the excavator

2.2. System Lifecycle Management With focus on the mentioned lifecycle phases, System

An important factor for the successful handling of product complexity is the closed-loop systems engineering approach (CLE), which aims at achieving the shortest possible development cycles and the highest possible quality assurance [17] with a minimized number of iteration [18]. Core ideas thereby are fast, digital information flows, for example of product changes or product use, and short access processes for product components (warranty processing, rotation stocks, material recovery), which can be backed by agile methods at the operational level [17]. Paquin [1] has identified the following three essential strategical steps, how best-in-class companies handle issues associated with complexity successfully:  Simulation: use of simulation as an essential enabler and its application as early as possible in development processes (system analysis & design phase)  Virtual prototypes: use of virtual prototypes (starting with formal, executable system models) for the

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simulation of preliminary designs (drafts) before physical prototypes are build  Improvement loops: completing the continuous improvement loop by recording the actual simulation results and feeding this information back in to calibrate and improve future simulations 3. System improvement concept for complex, multidisciplinary systems By applying the above-mentioned concepts of system lifecycle management and closed-loop systems engineering, this section presents a fully model-based approach for the optimization of today’s complex, multidisciplinary smart systems. The approach focuses on three specific improvement loops, which lay their improvement emphasis in the lifecycle phases of interdisciplinary system development, system verification and validation, and system usage (cf. Fig. 2).

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system behavior taking inputs to the system into consideration and producing outputs. Aim of the model execution is to assess how a system, particular object or phenomenon will behave over time [20]. Blanchard [21] describes the process of designing a model and its simulation as an experimental procedure with the purpose of either understanding the behavior of the system and/or evaluating various strategies for the operation of the system. The objective is to facilitate the analysis, synthesis, and/or evaluation of a system or a process by constructing and using a simplified representation of it. Either because the real system is not yet fully defined or available, or because it cannot be implemented directly due to cost, time, resources or risk constraints, model execution enables to gain system understanding by utilizing simulation at least for one of the following application scenarios [22]:     

Evaluate design alternatives Select the best set of parameters Verify system with its constraints Perform requirement compliance analysis Perform what-if analysis

3.2. Twin-in-the-loop Twin-in-the-loop is a new approach for validation of the digital twin of a system, its management platform as well as of concomitant business and service processes. This step can take place early in the system development phase, when the real system does not yet exist. It enables an early configuration of the digital twins and their associated business and service models by simulating the real field device and/or its data output by connecting a simulation-ready system model to an IoT operational system, in which the twins will be implemented later on (see Fig. 3) [17]. This approach brings the following advantages in the system development process: Fig. 2: Improvement loops in the system development in accordance to the Vmodel after VDI 2206 [32]

The three specified improvement steps are:   

Model-in-the-loop Twin-in-the-loop System-in-the-loop

 Increased dependability of the system design  Faster implementation of physical and digital subsystems dependent on not yet existent elements of the system  Faster handover of the system to the customer

The following subsection introduces the conceptual idea of the improvement steps, while section 4 clarifies the goal and application of the improvement concepts in the context of the given use case. 3.1. Model-in-the-loop Model-in-the-loop means the execution/simulation of a system model in the corresponding authoring modeling tool or in combination with an additional simulation tool [18,19] for the verification of the systems design in terms of architecture and behavior, and the early validation of partial solutions or system components. In general, a model describes a system entity, phenomenon or process by a physical, mathematical or logical representation. Model execution or simulation is the process by which a computer executes the instructions of the described

Fig. 3: Twin-in-the-loop concept

3.3. System-in-the-loop System-in-the-loop addresses the concept of system improvement by feedback of product/system usage information back into the development phase. Individual products or entire

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product populations are thereby optimized through planning strategies [23,24] and retrofitting concepts [25] closing the loop between system architecture and digital twins. For this purpose, operational data of field devices has to be collected, analysed and evaluated before it can be fed back into the development process as additional information (e.g. usage profiles, system load factors, utilization contexts, system failures, etc.). This requires a close integration between the PLM systems (core of engineering data management) and the IoT operational systems (core of operational data management). The data exchange between the system under development and the system in operation is described in Fig. 4. Actual configuration data:

 Bill of Materials / Technical drawings  Service/Maintenance information  Software

PLM push / IoT pull

Product Lifecycle Management System Virtual Product/ Digital Master       

Formal System models Modular construction kits, Variants 3D-Models, E/E, SW, Cybertronic Bill of Materials, Technical drawing Service/Maintenance information Order information Process information (Engineering Change, Issues etc.)

PLM pull / IoT push Results of analysis:

 Engineering Change information  Issue information  Usage information

IoT Operation System Device in field / Digital Twin     

Actual configuration Operating Data (Sensor data) State of health Measurement points, Sensors, Gateways Results of analysis

Fig. 4: Push & Pull concept between PLM and IoT

The system-in-the-loop approach is not applicable in new product/system development processes where previous product generations and/or on-field devices are not available. However, the concept can be implemented as soon as first products reach the field and provide usage data to be fed back into the still ongoing product development in order to implement optimizations through product reconfiguration [35] in the short term, and/or design changes in the longer term. 4. Closed-loop systems engineering in the context a test bed of an autonomous construction area In cooperation between the Institute of Virtual Product Engineering (VPE) at the University of Kaiserslautern and CONTACT Software Ltd, a research test bed on closed-loop system engineering has been set up to address the research questions mentioned earlier. This test bed of an autonomous construction area focusses on the model-based development, use and optimization of complex smart product systems from a methodological and information-technological perspective. Here, the close integration of development and operational/usage data is a key aspect. In order to validate the proposed solution, a smart excavator is defined as the smart product system to be considered. Thereby, the excavator represents a subsystem of the system of systems “autonomous construction area”. In the following subsections, the three improvement loops mentioned above will be demonstrated based on use case scenario through:  verification and validation of the system architecture and its behavior by a SysML-based simulation  validation of the digital excavator twin by a modelbased simulation

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 closing the gap between the field device, digital twin and system architecture of the excavator to optimize current systems and new product generations 4.1. Model-in-the-loop implementation Situated in the early development phase of system analysis and design, this improvement loop helps to verify and validate the system model of the excavator by applying an advanced model execution concept directly in the interdisciplinary modeling tool, Cameo Systems Modeler (CSM) from NoMagic [26]. The generated system model is expressed in the SysML [27] as modeling language and the VPEmecPro²methodology [28] as associated modeling approach. In the specific context of the excavator, the SysML-based simulation is used to check the expected system behavior with respect to excavator modes and working cycles. Figure 5 shows the essential parts of the simulation process:  executable system model in the authoring tool (1)  user interface (2)  time series chart (3) While the time series chart only serves to monitor the system parameters, the graphical user interface can be used not only to monitor the excavator, but also to control the transition between its different working modes (e.g. idling, travelling, trenching, etc.). In addition to the verification of the system to answer the question, whether the system is built correctly and/or behaves according to the expected behavior, the simulation also allows early validation of the system against its system requirements. 4.2. Twin-in-the-loop implementation While the system model was checked and improved in the model-in-the-loop simulation process, it serves as data basis for the validation of the digital twin and its IoT operating system during the twin-in-the-loop simulation process. As in the previous step, the ability to execute the system model is used but with a different purpose. In this case, the SysML-based simulation generates data, which is sent to the IoT operating system and represented by the therein-resident digital twin, simulating real a process with a field device. Consequently, the connection to the executable system model allows ensuring early on, whether the desired basic functions are present (e.g. data monitoring functions) and behave correctly. For example, it can be ensured at an early stage, whether specific business workflows (for example service and maintenance orders) are being triggered and executed at the correct moment and order. With regard to the application example of the excavator, the correct representation of the working cycle, the stroke values of the cylinders of the excavator arm or the operating time would correspond to the described data monitoring functions. The automated triggering of a workflow for a maintenance event would make sense if the excavator exceeds a certain operating time. An example of a service event would be the timely provision of fuel as soon as the fuel tank content of the excavator falls below a certain value.

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In order to implement the twin-in-the-loop concept, Cameo Systems Modeler (CMS) [26] has been linked to CONTACT Software Elements for IoT platform [7]. Figure 5-1 shows the executed system model in CSM. In Figure 5-2 the stroke values of the different hydraulic cylinders of the excavator arm during the trenching work cycle are depicted as output of the simulation. This graphical interface is also used to interact with

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the system model, i.e. to simulate the user interaction with the real system. Figure 5-3 depicts the data generated from the system model execution, which are than provided to the IoT platform through a web protocol. Figure 5-4 shows how the digital twin of the excavator in the IoT operational platform can be validated early on based on simulation data.

Fig. 5: Model-in-the-loop and Twin-in-the loop simulations based on executable SysML (1) model and IoT operational platform (2) (1) Executable SysML system model (Cameo Systems Modeler) (2) Web-based graphical interface for interaction of the user w/ the system model simulation (Cameo Systems Modeler) (3) Results of the model execution (here: time series charts) (Cameo Systems Modeler) (4) Representation of the simulation data in the digital twin in IoT platform (CONTACT Elements for IoT)

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4.3. System-in-the-loop implementation Once the development process has been completed and the smart product system and its digital image have been extensively tested, it can be produced and ultimately operationalized. During the operating phase, the smart product system generates data. These data are sent via the MQTT protocol to the digital twin managed by the IoT operating system, and provided to various stakeholders (users, maintenance or service technicians, device manager etc.) for various purposes. Fig. 6 shows CONTACT Software Elements for IoT platform [7] managing the digital twin of the real field device represented in the demonstrator example by a LEGO Technic / Mindstorms excavator. The donut diagram of the dashboard in the middle for example represents the percentage distribution of the operating time in the different working modes of the excavator. Such analyzed system data is of immense importance for the reconfiguration of particular field devices or of whole device populations to achieve performance improvement, or for the development of a new product generation for a particular market (continent, region, country, etc.).

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In order to integrate the usage data into the system model, the system architecture (Fig. 7, top left) is extended by the new element “usage profile”. This element should include all value properties, which are necessary for the system optimization based on usage data. In case of the excavator demonstrator, these values are operational time, distribution of operational time in excavator working modes and average energy consumption for the entire usage or broken down for each mode. In order to be able to analyze, evaluate and optimize the smart product system design in terms of actual use, the value properties of the usage profile must be linked to the system values or to the values of its corresponding subsystems and parts (Fig. 7, top right). The capability of multiple inheritance allows to define subclasses in SysML from one or more superclasses [29]. This technique permits to specify the usage profile for various purposes, like specific markets and its submarkets (Fig. 7, bottom).

Fig. 6: Collecting data from a real field device

The essential step is to re-integrate this information – evaluated and prepared – back into the development process so that it can be used already in the early phase of the system development to optimize the system design (architecture and behavior) by applying again the model-in-the-loop step. The System Lifecycle Management platform CONTACT Elements [7] supports the provision, management, analysis and feedback-loops of this information. A MBSE plug-in – a result of the German research project mecPro² [2] – allows to exchange data between the Cameo Systems Modeler as modeling tool and CONTACT Elements. This data include system requirements, entire SysML models and/or usage data necessary for the system-in-the-loop concept to be integrated into the system model. The basic concept for the integration of usage data is given in Fig. 7. It provides an overview of the model constructs and data objects necessary for the implementation of the concept and their incorporation in the system architecture at the given example of an excavator system.

Fig. 7: Integration of usage data into the SysML-based system model

Additionally, the usage profile of the excavator has two specific properties (dt_type and du_type). These serve the further limitation of the usage data to specific system configurations. While the driving unit type determines whether only chains, wheels or all drive types are to be considered, it is possible to select via the twin type, whether only the data of a specific twins (e.g. DT000027) or the data of the entire twin population shall be used to optimize the system design. 5. Conclusion and outlook Combining the concepts of system lifecycle management and closed-loop systems engineering the holistic approach

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introduced in the paper defines how smart product systems can be improved by using modeling, simulation and virtual prototypes as enabler. Above all, the resulting data are of particular importance for the system improvement. Whether from the simulation in the development phase or generated by the real system during product usage, the integration of these information into the system development by means of improvement loops achieves a huge benefit and helps to understand the complexity of the smart product systems and to make them more dependable. The presented concept for improvement of contemporary smart product systems includes three loops: (I) model-in-the-loop to improve the system design in terms of architecture and behavior by model execution; (II) twin-in-the-loop to improve the digital twin, its management platform and the corresponding business and service processes by using a simulation-based system model; (III) system-in-theloop to improve a field device or a further product generations by re-integrating the usage data into the development process. The holistic approach is evaluated methodically and information-technologically on an excavator as a smart subsystem of an autonomous construction area SoS at the research test bed on closed-loop system engineering. Further research on close-loop engineering will focus in particular on the transition from system design to the discipline-specific design and the integration of 1Dsimulations. The research test bed itself will be extended to the management of the entire life cycle data of the autonomous construction area SoS and its subsystems. References [1] Paquin R. The Systems Engineering Closed Loop Process: The Key for Validation. Aberdeen Group, 2014. [2] Eigner M, Koch W, Muggeo C (eds.). Modellbasierter Entwicklungsprozess cybertronischer Systeme – Der PLM-unterstützte Referenzentwicklungsprozess für Produkte und Produktionssysteme. Berlin, Heidelberg: Springer Vieweg, 2017. [3] Krause FL, Franke HJ, Gausemeier J (eds.). Innovationspotentiale in der Produktentwicklung. Carl Hanser Verlag, 2007. [4] Stark R, Krause FL, Kind C, et al. Competing in Engineering Design - the Role of Virtual Product Creation. CIRP Journal of Manufacturing Science and Technology, 3/2010. p. 175–184. [5] Gregorzik S. Smart Business Blueprints – Wie digitale Geschäfte im Internet der Dinge ins Laufen kommen. Contact Software White Paper, 2017. [6] https://vpe.mv.uni-kl.de [7] https://www.contact-software.com/en/products/ [8] Abramovici M. Smart Products. In: Laperrière L, Reinhart G, (eds). The International Academy for Produ, CIRP Encyclopedia of Production Engineering. Berlin, Heidelberg: Springer, 2015. [9] Porter ME, Heppelmann J. How Smart, Connected Products are Transforming Companies Technology & Operations. In: Harvard Business Review, 2015. [10] Lee EA. CPS foundations. In: Sepatnekar S (ed). Proceedings of the 47th Design Automation Conference (DAC), Anaheim CA, USA, June 13–18, 2010. ACM/IEEE, New York, 2010. p 737–742. [11] Rajkumar R, Lee I, Sha L, Stankovic J. Cyber-physical systems: the next computing revolution. In: Sepatnekar S (ed). Proceedings of the 47th Design Automation Conference (DAC), Anaheim CA, USA, June 13–18, 2010. New York: ACM/IEEE, 2010. p 731–736. [12] Mühlhäuser M. Smart Products: An Introduction. Communications in Computer and Information Science Constructing Ambient Intelligence, AmI 2007 Workshops Darmstadt, Germany, 2007. p. 158-164.

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