An Approach to Reduce Commissioning and Ramp-up time for Multi-variant Production in Automated Production Facilities

A key requirement for future production facilities is to perform new production processes in a flexible and adaptive way with available and known resources. In this context, a comprehensive description (ontology) of involved components has a high significance. If certain technological aspects are missing during a production process, the production control should respond in a dynamic, versatile and adaptive (agile) manner to the overall value network. The possibility to describe the requirements of products for the necessary processes in the same namespace like the requirements of the necessary processes for the resources is a prerequisite to enable this behavior. Afterwards the different requirements will be placed in relation to the respective requirements. The aim is to define the necessary processes for the production based on the description of the product and the known resources in an agile way. Due to this a framework for a comprehensive description of automated production facilities, products and processes is described in this paper. The idea is that based on this framework a production facility can change the produced products without dedicated commissioning and ramp-up phases

Model for web-application based configuration of modular production plants with automated PLC line control code generation

The international competition leads manufacturers in high-wage countries to focus more on high-value products, which often come at the disadvantage of small batch sizes. To remain competitive, the plant engineering for should be time and cost effective. One approach to achieve this are modular production lines. In the presented contribution, a product orientated web- service for the configuration of a modular production plant investigated. The resulting model then is interpreted by a code generator to generate a PLC line control. The approach is validated with a plant of metal hybrid carbon fiber seat rests

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations

Semantic Asset Administration Shells in Industry 4.0: A Survey

The Asset Administration Shell (AAS) is a fundamental concept in the Reference Architecture Model for Industry 4.0 (RAMI 4.0), that provides a virtual and digital representation of all information and functions of a physical asset in a manufacturing environment. Recently, Semantic AASs have emerged that add knowledge representation formalisms to enhance the digital representation of physical assets. In this paper, we provide a comprehensive survey of the scientific contributions to Semantic AASs that model the Information and Communication Layer within RAMI 4.0, and summarise and demonstrate their structure, communication, functionalities, and use cases. We also highlight the challenges of future development of Semantic AASs

Improving transferability between different engineering stages in the development of automated material flow modules

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from the electrical, mechanical or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g. module planning or functional engineering, which have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by AutomationML. Due to these different stages and the integration of several engineering disciplines, e.g. mechanical, electrical/electronic or software engineering, information not limited to one discipline are stored redundantly increasing the effort to transfer information and the risk of inconsistency. Although, data formats for the storage and exchange of plant engineering information exist, e.g. AutomationML, fixed domain specific structures and relations of the information, e.g. for automated material flow systems (aMFS), are missing. This paper presents the integration of a meta model into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from the coarse-grained plant planning to the fine-grained functional engineering.Comment: 11 pages, https://ieeexplore.ieee.org/abstract/document/7499821

A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories

An approach to open virtual commissioning for component-based automation

Increasing market demands for highly customised products with shorter time-to-market and at lower prices are forcing manufacturing systems to be built and operated in a more efficient ways. In order to overcome some of the limitations in traditional methods of automation system engineering, this thesis focuses on the creation of a new approach to Virtual Commissioning (VC). In current VC approaches, virtual models are driven by pre-programmed PLC control software. These approaches are still time-consuming and heavily control expertise-reliant as the required programming and debugging activities are mainly performed by control engineers. Another current limitation is that virtual models validated during VC are difficult to reuse due to a lack of tool-independent data models. Therefore, in order to maximise the potential of VC, there is a need for new VC approaches and tools to address these limitations. The main contributions of this research are: (1) to develop a new approach and the related engineering tool functionality for directly deploying PLC control software based on component-based VC models and reusable components; and (2) to build tool-independent common data models for describing component-based virtual automation systems in order to enable data reusability. [Continues.

Modeling and Simulation Methodologies for Digital Twin in Industry 4.0

The concept of Industry 4.0 represents an innovative vision of what will be the factory of the future. The principles of this new paradigm are based on interoperability and data exchange between dierent industrial equipment. In this context, Cyber- Physical Systems (CPSs) cover one of the main roles in this revolution. The combination of models and the integration of real data coming from the field allows to obtain the virtual copy of the real plant, also called Digital Twin. The entire factory can be seen as a set of CPSs and the resulting system is also called Cyber-Physical Production System (CPPS). This CPPS represents the Digital Twin of the factory with which it would be possible analyze the real factory. The interoperability between the real industrial equipment and the Digital Twin allows to make predictions concerning the quality of the products. More in details, these analyses are related to the variability of production quality, prediction of the maintenance cycle, the accurate estimation of energy consumption and other extra-functional properties of the system. Several tools [2] allow to model a production line, considering dierent aspects of the factory (i.e. geometrical properties, the information flows etc.) However, these simulators do not provide natively any solution for the design integration of CPSs, making impossible to have precise analysis concerning the real factory. Furthermore, for the best of our knowledge, there are no solution regarding a clear integration of data coming from real equipment into CPS models that composes the entire production line. In this context, the goal of this thesis aims to define an unified methodology to design and simulate the Digital Twin of a plant, integrating data coming from real equipment. In detail, the presented methodologies focus mainly on: integration of heterogeneous models in production line simulators; Integration of heterogeneous models with ad-hoc simulation strategies; Multi-level simulation approach of CPS and integration of real data coming from sensors into models. All the presented contributions produce an environment that allows to perform simulation of the plant based not only on synthetic data, but also on real data coming from equipments