Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line

Building a Knowledge Graph from Deviation Documentation for Problem-Solving on the Shop Floor

The description of deviations on the shop floor includes information about the deviation itself, possible causes and countermeasures. This information about current and already processed deviations and problems is a valuable source for future activities in the context of problem-solving and deviation management. However, extracting information from unstructured textual data is challenging. Furthermore, the relationships among the heterogeneous data are hard to represent. This paper proposes a framework to extract the knowledge contained in the deviation documentation and store it in a knowledge graph as triples. The proposed knowledge graph can then be used for the decision support system in production and will support more application scenarios in shop floor management in the future

Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review

Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS have been developed to structure the relationship between the digital and the physical worlds. However, there is no systematic review of the existing frameworks related to quality management in manufacturing CPS. Thus, our study aims at determining and comparing the existing frameworks. The systematic review yielded 38 frameworks analyzed regarding their characteristics, use of data science and Machine Learning (ML), and shortcomings and open research issues. The identified issues mainly relate to limitations in cross-industry/cross-process applicability, the use of ML, big data handling, and data security.publishedVersio

Comparison of AI-Based Business Models in Manufacturing: Case Studies on Predictive Maintenance

Recent advances in Artificial Intelligence extend the boundaries of what machines can do in all industries and business sectors. The economic potential to apply AI in manufacturing results in an increasing number of companies striving to gain a competitive advantage through AI and move into new markets. In this context, particular importance is given to the predictive maintenance of machines. Predictive maintenance promises the possibility of avoiding unexpected machine downtimes and thus increasing the availability of production lines. However, only a few machine manufacturers have a marketable offering of AI-based products or services in their portfolio. Even if technical feasibility is proven, companies lack an understanding of how to integrate AI solutions into new Business Models. This paper thus presents three case studies and their Business Models as examples. Practical considerations and recommendations on the strategical adoption of predictive maintenance technologies are derived

Building a knowledge graph from deviation documentation for problem-solving on the shop floor

The description of deviations on the shop floor includes information about the deviation itself, possible causes and countermeasures. This information about current and already processed deviations and problems is a valuable source for future activities in the context of problem-solving and deviation management. However, extracting information from unstructured textual data is challenging. Furthermore, the relationships among the heterogeneous data are hard to represent. This paper proposes a framework to extract the knowledge contained in the deviation documentation and store it in a knowledge graph as triples. The proposed knowledge graph can then be used for the decision support system in production and will support more application scenarios in shop floor management in the future

Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line

Getting Started: KI zum Nutzen der Industrie vorantreiben

Wichtig ist das Verständnis der verschiedenen Rollen und Fähigkeiten, die für ein Künstliche-Intelligenz (KI)-Projekt in der Industrie erforderlich sind [1]. Die Spezialisten aus dem Institut für Produktionsmanagement, Technologie und Werkzeugmaschinen (PTW) der Technischen Universität Darmstadt beschreiben im Beitrag sowohl die Vorgehensweise als auch die Zusammenarbeit der Rollen in den einzelnen Projektphasen. Das Vorgehen basiert auf der konsolidierten Methodik des „Cross Industry Standard Process for Data Mining“ (CRISP-DM) und seiner Erweiterung, der „Data Mining Methodology for Engineering Applications“ (DMME)

An AI Management Model for the Manufacturing Industry - AIMM

The use of artificial intelligence in manufacturing holds a multitude of potentials for improving the performance of a company in the dimensions time, quality, and cost. Many companies have recognized these possibilities, but only a few have already integrated this technology into their production. A major reason for this discrepancy is a lack of knowledge about necessary steps to conduct an AI project in order to solve an existing manufacturing problem. In literature, several models exist that provide structure and standards for the process of data mining in industrial applications (e.g. CRISP-DM, SEMMA, KDD). However, these process models have several shortcomings that prevent the effective usage in the manufacturing industry. The following paper addresses these shortcomings and proposes a holistic process model that shall serve as a standard management model for manufacturing companies to successfully introduce and apply AI as a production-related problem-solving tool. All three levels of the process model are presented, namely the strategic, tactical, and operational level. On the strategic level, an existing set of production problems is evaluated and prioritized concerning their feasibility and suitability for the application of AI. In the tactical part of the model, a solution for a selected problem is designed. Therefore, the problem understanding is deepened, infrastructural requirements are identified, and a financial evaluation of the developed solution is performed. The final, operational level focuses on the implementation of the developed solution to a finished AI application by a project team

Comparison of AI-Based Business Models in Manufacturing: Case Studies on Predictive Maintenance

Recent advances in Artificial Intelligence extend the boundaries of what machines can do in all industries and business sectors. The economic potential to apply AI in manufacturing results in an increasing number of companies striving to gain a competitive advantage through AI and move into new markets. In this context, particular importance is given to the predictive maintenance of machines. Predictive maintenance promises the possibility of avoiding unexpected machine downtimes and thus increasing the availability of production lines. However, only a few machine manufacturers have a marketable offering of AI-based products or services in their portfolio. Even if technical feasibility is proven, companies lack an understanding of how to integrate AI solutions into new Business Models. This paper thus presents three case studies and their Business Models as examples. Practical considerations and recommendations on the strategical adoption of predictive maintenance technologies are derived