Industry 4.0 production systems must support flexibility in various dimensions, such as for the products to be produced, for the production processes to be applied, and for the available machinery. ...In this article, we present a novel approach to design and control smart manufacturing systems. The approach is reactive, that is responds to unplanned situations and implements an iterative refinement technique, that is, optimizes itself during runtime to better accommodate production goals. For realizing these advances, we present a model-driven methodology and we provide a prototypical implementation of such a production system. In particular, we employ Planning Domain Definition Language (PDDL) as our artificial intelligence environment for automated planning of production processes and combine it with one of the most prominent Industry 4.0 standards for the fundamental production system model: IEC 62264. We show how to plan the assembly of small trucks from available components and how to assign specific production operations to available production resources, including robotic manipulators and transportation system shuttles. Results of the evaluation indicate that the presented approach is feasible and that it is able to significantly strengthen the flexibility of production systems during runtime. Note to Practitioners -Smart production is an umbrella for a number of shifts and initiatives that deal with digitization of manufacturing/production systems and related issues and potentials. In this work, we present an approach for utilizing automated planning for creating production plans. This is in contrast to the traditional approach, where recipes are programmed into the production system ahead-of-time. However, automated planning relies on specific languages and tools that are hard to master by nonexperts, which is a factor that strongly limited the utilization of plan-driven approaches for industrial automation in practice. Thus, we propose to generate planning tasks automatically with model-driven engineering techniques. We are utilizing the industrial standard IEC 62264 for the description of the production system, and the academic standard Planning Domain Definition Language (PDDL) for planning. PDDL is handled completely transparent to the user, that is the user is shielded from its complexity by employing the IEC 62264 model as the sole frontend.
•The article proposes an architecture and data models for integration of simulations and industrial SCADA systems.•The proposed solution combines a technical integration level and a semantic level ...for configuration of the technical level.•The knowledge for integration can be re-used for simulation design support and generating structures of simulation models.•The proposed approach reflects a simulation task definition and requirements on the management of simulations and data.
Stricter requirements on the quality of industrial plant operation together with environmental limits and decreasing energy consumption bring more complex automation systems. The intelligent control techniques, which are based on approaches from diverse disciplines including statistics, artificial intelligence or signal processing, have been widely used during the last years and their benefits have been proved. They cannot be developed and tested without simulation models and access to online and historical data. This article proposes a platform for the integration of simulations and industrial SCADA systems supporting complex data access and simulation code re-use.
The idea of the presented framework is to connect simulations, data sources, optimizers, other calculations and SCADA systems into one integrated environment seamlessly. A technical level of the framework provides integration of stakeholders and a semantic level captures engineering knowledge in inter-mapped ontologies and configures the technical level, which is often called model-driven configuration. The semantic level utilizes a formal model implemented as set of ontologies. The major contribution of the article are the layered model of the integration architecture and formulation of the integration requirements in the industrial automation domain. The proposed solution has been implemented and tested on a software prototype level. It is demonstrated on two use-cases covering both design and integration of simulation models from the industrial perspective. The proposed architecture is intended to be as general as possible, however it has been tested on signal-oriented simulators only. It is the main limitation of this contribution and it should be addressed in upcoming work.
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•The enhanced knowledge management improves the simulation design and integration.•The article proposes a data model supporting simulation model engineering.•An infrastructure called the Engineering ...Service Bus improves simulation processes.•The approach is evaluated on a hydraulic system use-case showing its efficiency.
Design of simulation models and their integration into industrial automation systems require knowledge from several heterogeneous data sources and tools. Due to the heterogeneity of engineering data, the integration of the tools and data are time-consuming and error-prone tasks nowadays. The key goal of this article is to provide an effective and efficient integration of heterogeneous data sources and tools as a knowledge basis to support dynamic simulation for industrial plants. The integrated knowledge is utilized both (i) in the design phase of simulation models for defining structure and interfaces of the models and (ii) in the runtime phase of industrial systems for model-driven configuration of the integrated environment. Reaching such goals with a manual approach or point-to-point integration is not beneficial as it may be possible for a few tools and data sources, but quickly gets very complex. A growing number of elements increases the risk of errors and the effort needed for integration. The proposed solution is based on a specification of a common data model to represent engineering knowledge and a service-oriented tool integration with the Engineering Service Bus. Engineering knowledge is integrated in a knowledge base implemented with ontologies in Web Ontology Language-Description Logic (OWL-DL). The proposed approach is demonstrated and evaluated on an educational hydraulic system. Major results of the article are: (i) a data model to represent engineering knowledge for dynamic industrial systems, (ii) the integration platform that, based on this model, integrates the tools for system design and runtime, and (iii) basic design-time and runtime processes for the integrated industrial simulations.
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Variability is crucial in the design of many advanced goods and it is also receiving increasing attention in production systems engineering. Since OPC Unified Architecture plays an important role ...when it comes to standardized information exchange in modern production systems, it can be a melting pot for information from various engineering domains, such as product design and production engineering — thus, it is an ideal place to hold variability information of products and production systems alike. Based on an initial variability information model we propose additional concepts for the persisting of configurations.
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Cardinality-based variability modeling with AutomationML Wimmer, Manuel; Novak, Petr; Sindelar, Radek ...
2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA),
2017-Sept.
Conference Proceeding
Variability modeling is an emerging topic in the general field of systems engineering and, with current trends such as Industrie 4.0, it gains more and more interest in the domain of production ...systems. Therefore, it is not sufficient to describe systems in several specific cases, but instead families of systems have to be used. In this paper we introduce a role class library for AutomationML to explicitly represent variability. This allows to exchange not only system descriptions but also system family descriptions. We argue for a light-weight extension of AutomationML. The variability-based modeling approach is based on cardinalities, which is a well-known concept from conceptual modeling and feature modeling. Furthermore, we also show how instantiations of variability models can be validated by our EMF-based AutomationML workbench.
A Graphical Toolkit for IEC 62264-2 Lang, Laurens; Wally, Bernhard; Huemer, Christian ...
Procedia CIRP,
2020, 2020-00-00, Volume:
93
Journal Article
Peer reviewed
Open access
Among the plethora of industrial standards available in the context of smart manufacturing, one series of standards is consistently being mentioned for dealing with manufacturing operations ...management: IEC 62264. Its second part provides a conceptual model for the description of production systems and their capabilities, including runtime information such as concrete maintenance schedules or achieved production goals. In this work, we present a concrete graphical syntax and toolkit for the creation and presentation of IEC 62264-2 compliant models, using techniques from model-driven (software) engineering. We have evaluated our tool by conducting a user study for assessing its usability and effectiveness.
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Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files ...that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge.
Industrial plants and processes are becoming very complex and sophisticated. Appropriate control algorithms, being necessary for their efficient operation, can be tested and fine-tuned on simulation ...models; for advanced process control the models are even necessary. Therefore, the fundamental precondition for control algorithm design is to implement and fine-tune a simulation model. Since plant description methods used nowadays are insufficient for describing modern flexible plants, this paper presents an ontology-based approach to the plant description. The proposed paradigm enables querying and inferring new pieces of knowledge. The solution supports the efficient integration of diverse engineering tools, such as simulators, SCADA systems, or even proprietary software used in particular projects. The proposed approach supports reasoning techniques and efficient utilization of domain specific languages for each engineer or expert. Ontologies comprise explicitly specified knowledge, enable to interchange it between automation tools and can guarantee consistency of the whole integrated solution. The presented approach can be used for both continuous and discrete systems, especially the large-scale ones. A practical example, describing a passive house with ontology individuals and generating a structure of a simulation model automatically by the implemented tool, is involved in this paper.
Production Planning with IEC 62264 and PDDL Wally, Bernhard; Vyskocil, Jiri; Novak, Petr ...
2019 IEEE 17th International Conference on Industrial Informatics (INDIN),
2019-July, Volume:
1
Conference Proceeding
Smart production systems need to be able to adapt to changing environments and market needs. They have to reflect changes in (i) the reconfiguration of the production systems themselves, (ii) the ...processes they perform or (iii) the products they produce. Manual intervention for system adaptation is costly and potentially error-prone. In this article, we propose a model-driven approach for the automatic generation and regeneration of production plans that can be triggered anytime a change in any of the three aforementioned parameters occurs.
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