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

Good Practices Guide: Systemic Approaches for a Circular Economy

This volume aims at clarifying the role of Systemic Design and Circular Economy Good Practices in the transition towards a sustainable development and how policy gaps can be addressed through the implementation of such examples. It is a guide to a selected range of Good Practices that address the most common policy gaps hampering the sustainable development; fostering all actors involved in policy making processes to encourage more effective paths towards the Circular Economy. This publication is addressed to regional policymakers and policy managers and is the second of a three book series published across a four-year period (2016–2020) as part of the RETRACE Project funded by the Interreg Europe Programme

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified

KNOWLEDGE BASE REPRESENTATION WITH AXIOMATIC DESIGN RULES FOR CONCEPT LEVEL UP IMPLEMENT IN ONTOLOGICAL FRAMEWORK

Axiomatic Design has been applied and developed as a tool, offering a scientific basis for design and improving design activities. Axiomatic Design has been used in various fields such as software system design, structure design, and product design. However, several challenges and limitations exist in Axiomatic Design including: the inconsistency in identifying design parameters, existence of coupled design, and multiple groups of functional requirements and design parameters. Aimed at using Axiomatic Design to generate conceptual solutions in engineering design while overcoming its limitations, a formal ontology is developed. The ontology defines functional requirements, design parameters, concepts, components and variables and their relationships. Axioms and rules of the Axiomatic Design ontology are discussed and summarized, which helps users understand the design issue deeply. The Axiomatic Design ontology is demonstrated to the car seat design as an example. Specific axioms and rules are generated and analyzed while the classes of concepts and components are built. With the help of the Axiomatic Design ontology and its axioms and rules, several example concepts are generated and then compared and analyzed. The Axiomatic Design ontology provides numerous design concepts and potentially helps users increase their creativity. The Axiomatic Design ontology allows coupling system to exist as the possible solutions. Besides, other factors need to be considered and other tools are necessary for evaluating design solutions

Knowledge management for bridge design process using building block concept

This thesis explores the concept of building blocks in the context of structural design within the Architecture, Engineering, and Construction (AEC) industry. The building block concept refers to the pieces of design knowledge from previous projects in the form of digital files that can be assembled and reused in future structural bridge projects. These building blocks consist of multiple types of file used in bridge design. The goal of the building block is to utilize the accumulated knowledge created from previous design projects to increase productivity and design quality of future ones. With that in mind, the thesis aims to establish the foundational theories of the building block concept in the knowledge management field, develop a conceptual framework, and create a proof-of-concept system. Literature in knowledge base development and knowledge reuse in the context of AEC design showed that reusing design knowledge requires both the knowledge content and the context surrounding it, which can be measured through the dimensions of abstraction and granularity. Both dimensions determine how relevant and reusable knowledge is to different users. The author concluded that the building block concept requires multiple levels of abstraction and granularity since no single level of context is adequate to cover all engineering knowledge. Moreover, the context information must be provided to different user's needs during the knowledge retrieval process by providing different browsing and querying interfaces. The implementation of the building block concept was done through the development of an ontological Expert System, capable of modeling the complex knowledge in structural design. The development focused on 2 stages: creating an informal model and translating that model to a machine-readable ontology. The informal model used the Icam DEFinition for Function Modeling (IDEF0) modeling method to represent the information requirements of the bridge design process. The build-ing blocks became the Inputs, Controls, Outputs, and Mechanisms (ICOM) of a design process. The ICOMs could be shared between different design processes, providing connection, relationships, and context information by association. This is the basis for the bridge design process assembly. The IDEF0 model was translated into the OWL language using Protégé to become a proof-of-concept. It demonstrated the extraction of building blocks associated with design processes through Protégé’s inferencing and querying capability with additional user inputs

Knowledge-driven Artificial Intelligence in Steelmaking: Towards Industry 4.0

With the ongoing emergence of the Fourth Industrial Revolution, often referred to as Indus-try 4.0, new innovations, concepts, and standards are reshaping manufacturing processes and production, leading to intelligent cyber-physical systems and smart factories. Steel production is one important manufacturing process that is undergoing this digital transfor-mation. Realising this vision in steel production comes with unique challenges, including the seamless interoperability between diverse and complex systems, the uniformity of het-erogeneous data, and a need for standardised human-to-machine and machine-to-machine communication protocols. To address these challenges, international standards have been developed, and new technologies have been introduced and studied in both industry and academia. However, due to the vast quantity, scale, and heterogeneous nature of industrial data and systems, achieving interoperability among components within the context of Industry 4.0 remains a challenge, requiring the need for formal knowledge representation capabilities to enhance the understanding of data and information. In response, semantic-based technologies have been proposed as a method to capture knowledge from data and resolve incompatibility conflicts within Industry 4.0 scenarios. We propose utilising fundamental Semantic Web concepts, such as ontologies and knowledge graphs, specifically to enhance semantic interoperability, improve data integration, and standardise data across heterogeneous systems within the context of steelmaking. Addition-ally, we investigate ongoing trends that involve the integration of Machine Learning (ML)techniques with semantic technologies, resulting in the creation of hybrid models. These models capitalise on the strengths derived from the intersection of these two AI approaches.Furthermore, we explore the need for continuous reasoning over data streams, presenting preliminary research that combines ML and semantic technologies in the context of data streams. In this thesis, we make four main contributions: (1) We discover that a clear under-standing of semantic-based asset administration shells, an international standard within the RAMI 4.0 model, was lacking, and provide an extensive survey on semantic-based implementations of asset administration shells. We focus on literature that utilises semantic technologies to enhance the representation, integration, and exchange of information in an industrial setting. (2) The creation of an ontology, a semantic knowledge base, which specifically captures the cold rolling processes in steelmaking. We demonstrate use cases that leverage these semantic methodologies with real-world industrial data for data access, data integration, data querying, and condition-based maintenance purposes. (3) A frame-work demonstrating one approach for integrating machine learning models with semantic technologies to aid decision-making in the domain of steelmaking. We showcase a novel approach of applying random forest classification using rule-based reasoning, incorporating both meta-data and external domain expert knowledge into the model, resulting in improved knowledge-guided assistance for the human-in-the-loop during steelmaking processes. (4) The groundwork for a continuous data stream reasoning framework, where both domain expert knowledge and random forest classification can be dynamically applied to data streams on the fly. This approach opens up possibilities for real-time condition-based monitoring and real-time decision support for predictive maintenance applications. We demonstrate the adaptability of the framework in the context of dynamic steel production processes. Our contributions have been validated on both real-world data sets with peer-reviewed conferences and journals, as well as through collaboration with domain experts from our industrial partners at Tata Steel

Software synthesis using generic architectures

A framework for synthesizing software systems based on abstracting software system designs and the design process is described. The result of such an abstraction process is a generic architecture and the process knowledge for customizing the architecture. The customization process knowledge is used to assist a designer in customizing the architecture as opposed to completely automating the design of systems. Our approach using an implemented example of a generic tracking architecture which was customized in two different domains is illustrated. How the designs produced using KASE compare to the original designs of the two systems, and current work and plans for extending KASE to other application areas are described

Capture and Maintenance of Constraints in Engineering Design

The thesis investigates two domains, initially the kite domain and then part of a more demanding Rolls-Royce domain (jet engine design). Four main types of refinement rules that use the associated application conditions and domain ontology to support the maintenance of constraints are proposed. The refinement rules have been implemented in ConEditor and the extended system is known as ConEditor+. With the help of ConEditor+, the thesis demonstrates that an explicit representation of application conditions together with the corresponding constraints and the domain ontology can be used to detect inconsistencies, redundancy, subsumption and fusion, reduce the number of spurious inconsistencies and prevent the identification of inappropriate refinements of redundancy, subsumption and fusion between pairs of constraints.EThOS - Electronic Theses Online ServiceGBUnited Kingdo