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Real-time co-simulation for the virtual commissioning of production systems

Procedia CIRP 2019年 79卷 397-402页

作者： Christian Scheifele Alexander Verl Oliver Riedel Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 Stuttgart 70174 Germany

During virtual commissioning, the real control technology is put into operation in advance using a model-based digital twin of the production system. The growing demands on the significance of digital twins result in the need for higher model depths and model scopes of the behavior models used. However, new approaches to enhance the usable computing power in a real-time simulation are required. This paper presents a real-time co-simulation platform for virtual commissioning. The platform includes the integration of powerful technology-specific simulation solutions based on integration interfaces and a real-time co-simulation architecture based on partitioning, parallelization, synchronization and data exchange mechanisms.

关键词： real-time co-simulation hardware-in-the-loop simulation virtual commissioning digital twin virtual CNC physics-based simulation

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Test case generation for production systems with model-implemented fault injection consideration

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Procedia CIRP 2019年 79卷 268-273页

作者： Karl Kübler Elmar Schwarz Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 70174 Stuttgart Germany

Complex production systems, which have to handle product diversity and short product life cycles, can only be stable and efficient when successfully tested prior to their start-up. Also, changes in automation software within the life cycle of a production system should be verified through repetitive testing before applying the new software version. Therefore, test automation in combination with fault injection on a virtual model of the production system is seen as the next step to improve the verification and validation of a production system. This work presents an approach to achieve negative test cases for a test automation framework.

关键词： Negative testing Test automation Virtual commissiong Hardware-in-the-loop

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Method for load-capable path planning in multi-axis fused deposition modeling

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Procedia CIRP 2019年 84卷 335-340页

作者： Frederik Wulle Martin Wolf Oliver Riedel Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 70174 Stuttgart Germany

Additive manufacturing (AM) technologies have evolved from a niche application for rapid prototyping into a crucial technological and economically significant industry for functional parts. However, many processes cannot guarantee the required component properties for industrial applications. Here, anisotropic strength properties introduced in Fused Deposition Modeling (FDM) are major constraints. These production-related strength properties originate in the weak bonds between plastic beads and thus between layers, which considerably affects the strength of the part in multi-axial stress conditions. Multi-axis FDM printing enables the diminishment of this effect by using Curved Layer Slicing. However, suitable path planning tools have not been developed to realize this approach on an industrial level. This paper describes a method for strength optimization under consideration of the manufacturing boundary conditions. This path planning method is based on a subdivision of the CAD part into sub-volumes, which comprise homogeneous sets of principal stress trajectories, as determined by the Finite Element Analysis. Subsequently, the sub-volumes are sliced individually using a Curved Layer Slicing approach and paths on the different layers are aligned with the principal stress trajectories. This method generates and uses strength-oriented meta data, which closes a crucial interface between CAD, CAM and AM processes.

关键词： Additive manufacturing Design optimization machine tool Polymer

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Production planning and control systems – a new software architecture Connectivity in target

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Procedia CIRP 2019年 79卷 361-366页

作者： Carsten Ellwein Anja Elser Oliver Riedel Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 D-70174 Stuttgart Germany

Since the rise of production planning and control systems those systems were subject to changes and further development due to new requirements and possibilities. Recently, Industry 4.0 and especially the cloud manufacturing paradigm brought up new requirements as connectivity demands, which are not fulfilled by current realizations of production planning and control systems. To adapt such systems to connectivity requirements, current software architectures have to be revised. Therefore, within this paper a new software architecture for production planning and control systems is introduced providing a basis for data exchange in-between such systems.

关键词： Planning control systems Software architecture Industry 4.0 Cloud manufacturing

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Automated OPC UA address space generation from existing data structures

Automated OPC UA address space generation from existing data...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Christian von Arnim Sebastian Friedl Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

If existing communication systems are converted to OPC UA, an OPC UA information model is required for the existing data structures. In small systems, an information model can be created manually with reasonable effort. In systems with numerous data structures, however, the effort for manual creation is too demanding. In order to continue using the existing data structures in the code without having to handle OPC UA-specific data structures, a transformation between the existing data structures and the OPC UA information model is necessary. This article presents a possibility of generating an OPC UA information model by a description of the existing data structures, e.g. generated based on ROS messages. The description of data structures is evaluated at runtime and linked with the existing data structures. The different representations for the different access types (read, request/response) in the OPC UA information model are discussed. In addition, an OPC UA client can read the information model from the descriptions of the data structures and write the read data back to the original data structure. In this way, both the provision of the data via OPC UA and the reception are possible. The adaptation effort of the existing software is minimal, since the existing structures are automatically populated and can still be used as usual.

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An evolutionary data model for the implementation of collective cloud manufacturing to maintain individual value-added networks

An evolutionary data model for the implementation of collect...

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IEEE Central America and Panama Convention (CONCAPAN)

作者： Matthias Milan Strljic Oliver Riedel Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

Cloud manufacturing (CM) offers the possibility to use and provide production services within an as-a-service concept. The CM paradigm enables more efficient resource utilization in the context of industry 4.0 through fully automated order processing. Existing architecture solutions, however, follow a central platform approach with a global neutral data model. As a consequence, the individuality of companies within the CM is not given any room for expression and symbioses between companies are not established. The result is a disadvantage particularly for SMEs, which generate their competitive advantage from innovative production technologies and symbiotic connections with customers and other SMEs. The focus of Collective Cloud manufacturing (CCM) is the preservation of these characteristics, in which an over-approximation of costs through the integration of production-related simulations is to be reduced and the data security of the customer is to be enhanced. So in this work, a data model for an existing concept for CCM is presented. The requirements for the data model and the runtime behavior are defined. Subsequently, the state of the art of existing models in the domain of CM is presented. The developed data model is presented with an application evaluation.

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Collective Cloud manufacturing for Maintaining Diversity in Production through Digital Transformation

Collective Cloud Manufacturing for Maintaining Diversity in ...

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IEEE Conference on Commerce and Enterprise Computing, CEC

作者： Matthias Milan Strljic Oliver Riedel Armin Lechler Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

In the area of production, individual domains are growing more and more closely together. Producing value-added chains and complex supply chains is becoming increasingly intertwined in the process of production planning. The exchange of data between the involved partners plays a critical role. The needs of the customer, the current order situation of the supplier as well as the manufacturing process dependencies of the manufacturer must be coordinated in a symbiotic manner to achieve efficient production. Therefore, this paper presents a variant of cloud manufacturing, collective cloud manufacturing, which focuses on the formation of cross-company symbioses to exchange production information in an environment isolated for data protection via an evolutionary needs model. For this purpose, a previously presented concept and the challenges of industry strategies are described to show how the concept of collective cloud manufacturing can meet the requirements of digitalization.

关键词： Production manufacturing Cloud computing Data models Solid modeling Companies

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From monolith to microservices: A container-based en 61131-3 architecture 48

From monolith to microservices: A container-based en 61131-3...

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48th International Conference on Computers and Industrial engineering, CIE 2018

作者： Tasci, T. Walker, M. Riedel, O. Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

Internet-of-Things is gaining importance in the field of industrial automation. Furthermore, for long-term success in dynamic markets producing companies need more and more agile and flexible production systems. Therefore, Programmable Logic controllers (PLCs) as main parts of automation in production systems also need to become more flexible. Today’s PLCs are monolithic and therefore can only deployed as a single piece of software. In this paper, we present a container-based real-time-capable modularized control architecture that uses messaging for inter-module communication and introduces a hardware abstraction layer, in order to increase flexibility, reusability, portability and maintainability. With a benchmark performed on a prototypical implementation, we validate the feasibility of the proposed architecture for practical real-time automation tasks. © 2018, Curran Associates Inc. All rights reserved.

关键词： Containers

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Production planning and control systems – Breakage in connectivity make them fit to fit altering conditions 18

Production planning and control systems – Breakage in conne...

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2018 International Conference on Computers in Management and Business, ICCMB 2018

作者： Ellwein, Carsten Elser, Anja Riedel, Oliver Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781450364232

Since the rise of production planning and control systems those systems were subject to changes and further development due to new requirements and possibilities. This necessitates to continuously keep track of current requirements and to guarantee that they are fulfilled. In this paper upcoming requirements in the context of Industry 4.0 and especially the cloud manufacturing paradigm have been elaborated on production planning and control systems. It could be shown that current realizations do not fulfil these demands, especially not connectivity demands striven by the fourth industrial revolution. From this finding several requirements for an up to date production planning and control system are derived. © 2018 Association for Computing machinery.

关键词： control systems

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A data model for data gathering from heterogeneous IoT and Industry 4.0 applications

A data model for data gathering from heterogeneous IoT and I...

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18th International Stuttgart Symposium on Automotive and Engine Technology, 2018

作者： Strljic, Matthias Milan Tasci, Timur Schmidt, Alexander Korb, Tobias Riedel, Oliver Institute of Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Germany

ISBN: (纸本)9783658211936

Industry 4.0 (I4.0) offers the opportunity to gain a detailed insight into the current production process by means of an increased networking of production plants. This crosslinking makes it possible to record the entire state of a production plant and to trace it within a later analysis. The aim of this analysis is to optimize the monitored production process resulting from analyses of I4.0 value-adding services [1, 2]. Figure 1 schematically visualizes the information flow for such a scenario. Data from the various levels of production are collected, stored in a data storage facility and evaluated by a valueadding service pipeline. The results are integrated back into the production process as optimizations. In this work, first the requirements for such a value-adding service pipeline are determined, which results in a total of five requirements and is abbreviated with R1 to R5. Subsequently, a suitable system architecture from the Big Data area is selected in order to meet the previously established requirements and thus implement a value-adding service pipeline. The requirements R1 - R5 and the system architecture will then flow into a data model for data acquisition and transmission within the shop floor of the production. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2018.

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