Knowledge Integration in Smart Factories

Abstract

Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

“…To tackle the problem of standard operability amongst different standardization frameworks and device knowledge, a knowledge-driven approach is developed as industrial 4.0 knowledge-graph (I40KG) as universal description of standards and standardization framework as I40KG integrate more than 200 standards and standardization frameworks. Ontotext I40KG platform is a sematic approach as integration of standardization efforts and reference initiatives into enterprise industrial knowledge graph [18] . The industry 4.0 knowledge graph ontology is utilized for the representation of main characteristics of industry 4.0 as standards, norms and reference frameworks and the relationship between them.…”

Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval

“…A KG is a network of interconnected entities, where each entity is represented by a graph node, and the relevance between the nodes is represented by relations [18]. Through the nodes and their relations, the KG is described as a set of triplets (head, relation, tail) as shown in Equation (1).…”

“…The important role of data is motivated by the potential of extractable knowledge hidden within it. Knowledge integration plays an important role in smart factories on a human, organizational, and technical level, where data is the central resource for extracting knowledge with the help of, e.g., data analytics or machine learning methods [1]. For this reason, modern decision-making and information-retrieval systems provide new methods for extracting this knowledge from data.…”

“…We address these challenges by developing a framework that can link the IR and explainability algorithms to the domain requirements through a knowledge graph as a unified knowledge representation structure. We aim to integrate those requirements in the knowledge representation to: (1) allow the intelligent algorithms to utilize the domain's requirements inherently, since they are already integrated in the knowledge structure, and (2) provide a domain-agnostic semantic knowledge structure that both IR and explainability algorithms can use. The key concept in our approach that answers these questions is considering a semantic knowledge graph (KG) representation as a single source of truth (SSoT).…”

Experimental Evaluation and Modeling of the Accuracy of Real-Time Locating Systems for Industrial Use

“…The increasing complexity of industrial systems and production processes in smart factories is pushing towards operational optimization strategies based on the centralization and integration of available information [5]. This information is gathered from the multiple control and monitoring systems available at all levels of the manufacturing process as defined by the automation pyramid [6]: from top management and planning levels, at which enterprise resource planning (ERP), manufacturing execution system (MES), or warehouse management system (WMS) information is available; to bottom control and field level, at which operational control data from shop floor machinery is directly collected from controllers, sensors, and actuators.…”