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

Methods for Semantic Interoperability in AutomationML-based Engineering

Industrial engineering is an interdisciplinary activity that involves human experts from various technical backgrounds working with different engineering tools. In the era of digitization, the engineering process generates a vast amount of data. To store and exchange such data, dedicated international standards are developed, including the XML-based data format AutomationML (AML). While AML provides a harmonized syntax among engineering tools, the semantics of engineering data remains highly heterogeneous. More specifically, the AML models of the same domain or entity can vary dramatically among different tools that give rise to the so-called semantic interoperability problem. In practice, manual implementation is often required for the correct data interpretation, which is usually limited in reusability. Efforts have been made for tackling the semantic interoperability problem. One mainstream research direction has been focused on the semantic lifting of engineering data using Semantic Web technologies. However, current results in this field lack the study of building complex domain knowledge that requires a profound understanding of the domain and sufficient skills in ontology building. This thesis contributes to this research field in two aspects. First, machine learning algorithms are developed for deriving complex ontological concepts from engineering data. The induced concepts encode the relations between primitive ones and bridge the semantic gap between engineering tools. Second, to involve domain experts more tightly into the process of ontology building, this thesis proposes the AML concept model (ACM) for representing ontological concepts in a native AML syntax, i.e., providing an AML-frontend for the formal ontological semantics. ACM supports the bidirectional information flow between the user and the learner, based on which the interactive machine learning framework AMLLEARNER is developed. Another rapidly growing research field devotes to develop methods and systems for facilitating data access and exchange based on database theories and techniques. In particular, the so-called Query By Example (QBE) allows the user to construct queries using data examples. This thesis adopts the idea of QBE in AML-based engineering by introducing the AML Query Template (AQT). The design of AQT has been focused on a native AML syntax, which allows constructing queries with conventional AML tools. This thesis studies the theoretical foundation of AQT and presents algorithms for the automated generation of query programs. Comprehensive requirement analysis shows that the proposed approach can solve the problem of semantic interoperability in AutomationML-based engineering to a great extent

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.

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE

Proceedings of the 4th Workshop of the MPM4CPS COST Action

Proceedings of the 4th Workshop of the MPM4CPS COST Action with the presentations delivered during the workshop and papers with extended versions of some of them

An ontology-based approach for integrating engineering workflows for industrial assembly automation systems

Modern manufacturing organisations face a number of external challenges as the customer-base is more varied, more knowledgeable, and has a broader range of requirements. This has given rise to paradigms such as mass customisation and product personalisation. Internally, businesses must manage multidisciplinary teams that must work together to achieve a common goal despite spanning multiple domains, organisations, and due to improved communication technologies, countries. The motivation for this research is to therefore understand firstly how the multiplicity of stakeholders come together to realise the ever increasing and ever more complex number of product variants that manufacturing systems must now realise. The lack of integration of engineering tools and methods is identified to be one of the barriers to smooth engineering workflows and thus one of the key challenges faced in the current dynamic market. To address this problem, this research builds upon previous works that propose domain ontologies for representing knowledge in a way that is both machine and human readable, facilitating interoperability between engineering software. In addition to this, the research develops a novel Skill model that brings the domain ontologies into a practical, implementable framework that complements existing industrial workflows. The focus of this thesis is the domain of industrial assembly automation systems due to the role this stage of manufacturing plays in realising product variety. Therefore, the proposed ontological models and framework are applied to product assembly scenarios. The key contributions of this work are the consolidation of domain ontologies with a Skill model within the context of assembly systems engineering, development of a broader framework for the ontologies to sit within that complements existing workflows. In addition, the research demonstrates how the framework can be applied to connect assembly process planning activities with machine control logic to identify and rectify inconsistencies as new products are introduced. In summary, the thesis identifies the shortcomings of existing ontological models within the context of manufacturing, develops new models to address those shortcoming, and develops new, useful ways for ontological models to be used to address industrial problems by integrating them with virtual engineering tools

Implementing Shop Floor IT for Industry 4.0

The fourth industrial revolution, Industry 4.0, is a paradigm shift that is currently changing our society and the way we produce things. The first industrial revolution started at the end of the 18th century and was enabled by mechanisation and steam power. The spread of electricity enabled assembly lines and mass production during the first half of the 20th century, which was the second industrial revolution. Industry 3.0 came with the invention of the computer with an increase of automation such as programmable machines and robots. The fourth revolution is upcoming and is supposed to increase productivity and flexibility to the same extent as the previous three. The idea is to utilise recent advances in information technologies and the Internet to interconnect machines, tools, equipment, sensors, and people into decentralised intelligent systems that can sense and adapt to the environment.The term Industry 4.0 was introduced 2011 by the German government as a national programme to boost research and development of the manufacturing industry. Many countries with, including Sweden, has since then started similar initiatives. The aim is to prevent further outsourcing of production to low-cost countries by improving competitiveness with increased automation and flexibility. However, the implementation is slow and many manufacturing companies have only started to computerise and are far from digitalised. There are many challenges in terms of technology, people, and organisation. Many manufacturing companies do not know how to start the process of digitalisation, they lack the knowledge and the organisation.To implement a production environment according to the Industry 4.0 vision the manufacturing organisation and its view on technologies need to change. Part of this change is to design an information technology architecture that enables interconnection of machines, equipment, tools, and people on the shop floor. The aim of this thesis is to aid decision makers in the manufacturing industry to implement a shop floor IT according to the Industry 4.0 paradigm. This was achieved with the design science approach, which means that the researcher has implemented different artefacts (technologies) that have been evaluated. The work is based on six studies that connect to real problems found in the industry today. These studies are presented and discussed with respect to three research questions: important aspects, technological implementations, and effects. Results include concrete and practical examples of how to implement IT artefacts for the shop floor. Furthermore, it highlights the complexity of the problem and shows the need for a holistic and incremental approach

Formal Digital Description of Production Equipment Modules for supporting System Design and Deployment

The requirements for production systems are moving towards higher flexibility, adaptability and reactivity. Increasing volatility in global and local economies, shorter product life cycles and the ever-increasing number of product variants arising from product customization have led to a demand for production systems which can respond more rapidly to these changing requirements. Therefore, whenever a new product, or product variant, enters production, the production system designer must be able to create an easily-reconfigurable production system which not only meets the User Requirements (UR) but is quick and cost-efficient to set up. Modern production systems must be able to integrate new product variants with minimum effort. In the event of a product changeover or an unforeseen incident, such as the mechanical failure of a production resource, it must be possible to reconfigure the production system smoothly and seamlessly by adding, removing or altering the resources. Ideally, auto-configuration should obviate the need to manually re-programme the system once it has been reconfigured.The cornerstone of any solution to the above-mentioned challenges is the concept of being able to create formalised, comprehensive descriptions of all production resources. Providing universally-recognised digital representations of all the multifarious resources used in a production system would enable a standardised exchange of information between the different actors involved in building a new production system. Such freely available and machine-readable information could also be utilised by the wide variety of software tools that come into play during the different life cycle phases of a production system, thus considerably extending its useful life. These digital descriptions would also offer a multi-faceted foundation for the reconfiguration of production systems. The production paradigms presented here would support state-of-the-art production systems, such as Reconfigurable Manufacturing Systems (RMSs), Holonic Manufacturing Systems (HMSs) and Evolvable Production Systems (EPSs).The methodological framework for this research is Design Research Methodology (DRM) supported with Systems Engineering, Action Research, and case-based research. The first two were used to develop the concept and data models for the resource descriptions, through a process of iterative development. The case-based research was used for verification, through the modelling and analysis of two separate production systems used in this research. The concept, on which this thesis is based, is itself based on the triplicity of production system design, i.e. Product, Process and Resource. The processes, are implemented through the capabilities of the resources, which are thus directly linked to the product requirements. The driving force behind this new approach to production system design is its strong emphasis on making production systems that can be reconfigured easily. Successful system reconfiguration can only be achieved, however, if all the required production resources can be quickly and easily compared to all the available production resources in one unified, and universally accepted form. These descriptions must not only be able to capture all of a production system’s capabilities, but must also include information about its interfaces, kinematics, technical properties and its control and communication abilities.The answer to this lies in the Emplacement Concept, which is described and developed in this thesis. The Emplacement Concept proposes the creation of a multi-layered Generic Model containing information about production resources in three different layers. These are the Abstract Module Description (AMD), the Module Description (MD), and the Module Instance Description (MID). Each of these layers has unique characteristics which can be utilised in the different phases of designing, commissioning and reconfiguring a production system. The AMD is the most abstract (general) descriptive layer and can be used for initial system design iterations. It ensures that the proposed resources for the production system are exchangeable and interchangeable, and thus guides the selection of production resources and the implementation (or reconfiguration) of a production system. The MD is the next level down, and provides a more detailed description of the type of production resource, providing ’finer granularity’ for the descriptions. The MID provides the finest level of granularity and contains invaluable information about the individual instances of a particular production resource. This research involves two practical implementations of the Generic Model. These are used to model and digitally represent all the production resources used in the two use-case environments. All the modules in the production systems (25 in all) were modelled and described with the data models developed here. In fact, we were able to freeze the data models after the first case study, as they didn’t need any major changes in order to model the production resources of the second use-case environment. This demonstrates the general applicability of the proposed approach for modelling modular production resources.The advantages of being able to describe production resources in a unified digital form are many and varied. For example, production systems which are described in this way are much more agile. They can react faster to changes in demand and can be reconfigured easily and quickly. The resource descriptions also improve the sustainability of production systems because they provide detailed information about the exact capabilities and characteristics of all the available resources. This means that production system designers are better placed to utilise ready-made modules, (design by re-use). Being able to use readily available production modules means that the Time to Market and Time to Volume are improved, as new production systems can be built or reconfigured using tested and fully operational modules, which can easily be integrated into an already operational production system. Finally, the resource descriptions are an essential source of information for auto-configuration tools, allowing automated, or semi-automated production system design. However, harvesting the full benefits of all these outcomes requires that the tools used to create new production systems can understand and utilise the modular descriptions proposed by this concept. This, in turn, presupposes that the all the formalised descriptions of the production modules provided here will be made publicly available, and will form the basis for an ever-expanding library of such descriptions