Knowledge Graphs in Digital Twins for AI in Production

Part 4: Learning and Robust Decision Support Systems for Agile Manufacturing environmentsInternational audienceAI is increasingly penetrating the production industry. Today, however, AI is still used in a limited way in a production environment, often focusing on a single production step and using out-of-the-box AI algorithms. AI models that use information spanning a complete production line and even larger parts of the product lifecycle could add significant value for production companies. In this paper, we suggest a digital twin architecture to support the complete AI lifecycle (discovering correlations, learning, deploying and validating), based on a knowledge graph that centralizes all information. We show how this digital twin could ease information access to different heterogenous data sources and pose opportunities for a wider application of AI in production industry. We illustrate this approach using a simplified industrial example of a compressor housing production, leading to preliminary results that show how a data scientist can efficiently access, through the knowledge graph, all necessary data for the creation of an AI model

Domain Models and Data Modeling as Drivers for Data Management : The ASSISTANT Data Fabric Approach

To develop AI-based models capable of governing or providing decision support to complex manufacturing environments, abstractions and mechanisms for unified management of data storage and processing capabilities are needed. Specifically, as such models tend to include and rely on detailed representations of systems, components, and tools with complex interactions, mechanisms for simplifying, integrating, and scaling management capabilities in the presence of complex data requirements (e.g., high volume, velocity, and diversity of data) are of particular interest. A data fabric is a system that provides a unified architecture for management and provisioning of data. In this work we present the background, design requirements, and high-level outline of the ASSISTANT data fabric - a flexible data management tool designed for use in adaptive manufacturing contexts. The paper outlines the implementation of the system with specific focus on the use of domain models and the data modeling approach used, as well as provides a generic use case structure reusable in many industrial contexts

Multi-agent coordination and control system for multi-vehicle agricultural operations

A multi-agent coordination and control system is deployed for controlling multiple interacting agricultural vehicles involved in the crop harvesting process. Crops are gathered by combine harvesters. The harvested product is transferred to one or more tractors every time the combine harvester’s storage capacity is reached. Good cooperation between the combine harvesters and the tractors is important for successfully completing the harvesting process. The multi-agent system allows concurrent planning and execution of the process, aiming to increase efficiency of the vehicles and improve cooperation between them. The planning is performed by short-term operational forecasting. The system provides detailed instructions and guidance to the operators of the individual vehicles (combine harvesters and tractors) by means of a graphical user interface. The state updates from the agricultural vehicles are considered by the multi-agent system, which dynamically updates the control instructions. In this way the control instructions for the agriculture vehicles remain valid and effective throughout the process.status: publishe

Towards a knowledge graph framework for ad hoc analysis in manufacturing

Abstract: The development of artificial intelligence models for data driven decision making has a lot of potential for the manufacturing sector. Nevertheless, applications in industry are currently limited to the actionable insights one can discover from the available data and knowledge of a manufacturing system. We call the process to obtain such insights \u201cad hoc analysis\u201d. Ad hoc analysis at system level is very complex in an industrial setting due to the inherent heterogeneity of data and existence of data silos, the lack of information and knowledge formalization, and the inability to meaningfully and efficiently reason about the data, information and knowledge. In this paper, we provide and outline a framework for ad hoc analysis in manufacturing based on knowledge graphs and influenced by the metamodelling paradigm. We derive its requirements and key elements from an analysis of several industry application cases. We show how manufacturing data, information and knowledge can be combined and made actionable using this framework. The framework supports workflows and tools for the data consumer (i.e., data scientist), and for the knowledge engineer. Furthermore, we show how the framework is integrated with existing data sources. Then, we discuss how we applied the framework to several application cases. We discuss how the framework contributes when applied, and what challenges still remain