Fusion of Knowledge Graphs into Cognitive Modular Production

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Ibrahim Yitmen	--	2058	2023-09-21 13:09:32	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	2058	2023-09-22 16:12:01	\|

This entry is adapted from the peer-reviewed paper 10.3390/buildings13092306

Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems.

knowledge graph digital twin modular production cognitive modular production

1. Introduction

In recent years, the fusion of Cyber–Physical Production Systems (CPPSs) with cognitive technologies has enabled a new era of intelligent manufacturing ^[1]^[2]. The CPPS is a concept that refers to the fusion of physical systems (such as machines and equipment) with digital systems (such as sensors, data analytics, and control systems) in the production process ^[3]^[4]. The CPPS allows for the real-time monitoring, control, and optimization of the production process. The incorporation of cognitive technologies, like artificial intelligence (AI) and machine learning (ML), within the CPPS has allowed for the creation of intelligent agents that can adapt and learn from their environment, optimizing production processes and reducing costs ^[5]. However, to fully realize the potential of cognitive technologies in the CPPS, the seamless fusion of various modules is crucial. This is where knowledge graphs (KGs) come into play.

KGs have become an important tool for organizing and managing data in a way that allows for the fusion of different modules in a seamless manner ^[6]^[7]. KGs are structured representations of knowledge that capture relationships between entities, enabling the creation of intelligent agents that can reason and learn from data ^[8]. Cognitive digital twins (CDTs), introduced by Eirinakis et al. ^[9] and Abburu et al. ^[10], are virtual replicas of physical systems or processes that are created by integrating data from various sources, such as sensors, machine logs, and historical data. CDTs are designed to simulate and predict the behavior of the physical system and can be used to identify inefficiencies or problems in the production process ^[11]^[12]. KGs have the potential to be incorporated into CDTs as a way of organizing and representing the data that are used to create the virtual model. By structuring the data in a way that captures the relationships between different entities, a KG can improve the accuracy and reliability of predictions made by the CDT ^[13]. This can help manufacturers make better decisions and optimize their production processes based on the insights provided by the CDT ^[14]. In essence, CDTs with KGs can act as a decision support tool for manufacturers, allowing them to anticipate and mitigate potential problems, reduce downtime, and optimize their production processes ^[11]^[15]^[16].

Additionally, cognitive modular production (CMP) is an emerging approach to manufacturing that combines the benefits of CPPSs and CDTs to create a more intelligent and automated manufacturing system ^[6]. CMP involves breaking down the manufacturing process into smaller modular components, each of which can be optimized individually to improve the overall performance. This approach allows for greater flexibility and agility in the manufacturing process, as well as improved efficiency and reduced downtime. By using CDTs, CMP allows for virtual testing and the simulation of the modular components before they are deployed, while the CPPS enables the monitoring and control of the physical production process in real time ^[9]^[11].

The fusion of a KG into CMP has the potential to transform the way manufacturing processes are conducted. The ability to seamlessly integrate different modules and create intelligent agents that can reason and learn from a large amount of data can lead to a more efficient and effective production process. However, despite the potential benefits of integrating KGs into CMP, there is a lack of knowledge in terms of the understanding of the methods and techniques that can be used to effectively integrate KGs into CMP. Existing research has primarily focused on the individual components of CMP, such as the use of AI and the Internet of Things (IoT) for autonomous operations, but the potential benefits of incorporating KGs into CMP have not been thoroughly investigated. KGs offer a unique approach to organizing and representing complex data, which could significantly enhance the decision-making processes in CMP systems ^[6]^[17]^[18]^[19]. However, there is a lack of empirical evidence on the effectiveness of integrating KGs into CMP and the potential benefits that this fusion could bring to the industrialized building industry.

2. Theoretical Background

2.1. Cognition in Modular Production

Modular production is a manufacturing approach that involves creating a product by assembling standardized, pre-made components or modules. These modules are designed to fit together seamlessly and can be easily interchanged, allowing for flexibility and customization in the production process ^[19]^[20]. The use of modular production can lead to increased efficiency, reduced costs, and an improved product quality. Additionally, the ability to quickly modify and adapt production systems to changing market demands makes modular production an attractive option for manufacturers in a variety of industries ^[16].

Cognitive modular production (CMP) refers to the fusion of cognitive abilities, such as perception, attention, memory, and decision making, into the production process to enhance its efficiency and quality. This approach involves the fusion of AI, ML, and the IoT into modular production systems to create intelligent production modules and as a result improve the speed, accuracy, and adaptability of the production process ^[21]. Furthermore, modular production systems often lack standardization, which can make it difficult to integrate cognitive processes in a standardized and consistent manner. Another obstacle to integrating cognition into modular production systems is the need for specialized knowledge and expertise in both cognitive science and modular production systems ^[22]^[23].

2.2. CDT Application on the Production Line of Modular Production Systems

CDTs are a relatively new technology that combines the concepts of digital twins (DTs) ^[24] and cognitive computing ^[25]. CDTs have the potential to revolutionize the way decisions are made on the production line of modular production systems by enabling real-time decision making based on the analysis of large amounts of data ^[9]. CDTs can provide a real-time view of the production process and its performance, enabling operators to make informed decisions based on the current state of the system ^[26]. They can also provide insights into potential issues or opportunities for optimization, allowing for proactive decision making rather than reactive responses to problems. Additionally, CDTs can be used to simulate different scenarios, enabling operators to evaluate the impact of potential decisions before implementing them on the production line ^[23].

One application of CDTs in modular production systems is in predictive maintenance. By analyzing data from sensors and other sources, a CDT can predict when a machine or component is likely to fail, allowing operators to schedule maintenance proactively and minimize downtime ^[15]^[27]. This can lead to significant cost savings and increased productivity. Another application of CDTs is in quality control. By analyzing data from sensors and cameras, a CDT can detect defects in real time and provide feedback to operators. This can enable operators to quickly identify and resolve issues, reducing scrap and reworkings ^[23].

CDTs can also be used to optimize the production process. By analyzing data on factors such as machine utilization, production rates, and energy consumption, a CDT can identify opportunities for optimization and provide recommendations to operators. This can lead to improved efficiency and reduced costs ^[21]^[28]. However, CDT technology is still in early stages and has limited application in modular production systems. There are some challenges to implementing CDTs in modular production systems. One challenge is the availability and quality of data. CDTs rely on high-quality data to provide accurate predictions and recommendations. If data are incomplete, inaccurate, or unavailable, the performance of the CDT can be compromised ^[23]. Another challenge is the complexity of the production system.

Modular production systems often involve a large number of components and processes that must be coordinated. This can make it difficult to develop a CDT that accurately models the system and provides meaningful insights. To address these shortcomings, researchers and practitioners in the field of CDTs are working on developing standardized data formats and interfaces, as well as addressing issues around data privacy and security.

2.3. Establishment of Production Line and CDT and Their Implementation

In order to integrate CDTs into the physical system, it is important to have a clear understanding of the system’s architecture and components. This can involve creating a detailed schematic of the system and mapping out the flow of materials, information, and energy through the system ^[15]. Once the digital model has been created, data must be collected to create the DT. These data may come from a variety of sources, including historical performance data, real-time sensor data, and information on the performance characteristics of different components ^[28]. The DT can then be used to simulate different scenarios and test potential changes or improvements to the system. For example, the DT could be used to simulate the impact of introducing a new machine or process into the production environment or to test the impact of changing the layout of the production line.

In a modular production environment, implementing a production line system that incorporates CDTs involves integrating the DT with the physical system. This may involve installing sensors and other monitoring devices to collect data on the performance of different components and processes and integrating these data into the digital twin in real time ^[29].

One of the key benefits of using CDTs in a modular production environment is the ability to quickly reconfigure the system to accommodate changing production needs. For example, if a particular product is in high demand, the system can be reconfigured to prioritize the production of that product while minimizing the impact on other products being produced on the same line. To support this level of flexibility, it is important to have a modular system architecture that allows for easy reconfiguration and adaptation. This may involve the use of standardized components and interfaces, as well as the use of flexible manufacturing cells that can be easily reconfigured to support different production requirements ^[28].

2.4. KG Representation for CMP

In recent years, the use of KGs has become increasingly popular, particularly in the field of artificial intelligence and data science. KGs provide a way to represent and organize complex information and knowledge in a way that is both intuitive and computationally tractable ^[8]. In the context of CMP systems, KGs can be used to capture information about the system’s components, processes, and interactions, as well as the relationships between them. To create a KG for a CMP system, one would first need to identify the relevant entities and relationships within the system. This might include information about the physical components of the system (e.g., machines, sensors, and actuators), the processes that take place within the system (e.g., assembly, machining, and inspection), and the various data streams that are generated by the system (e.g., sensor readings, control signals, and production data) ^[11]^[27]^[30].

Once the relevant entities and relationships have been identified, they can be represented as nodes and edges in the KG. Each node would represent a specific entity within the system and each edge would represent a relationship between two entities (e.g., a machine is connected to a production line or a sensor provides data to a control system).

By organizing information in this way, a KG has the potential to provide a powerful tool for decision making and interoperability within a CMP system. For example, one could use a KG to identify potential inefficiencies in the production process or to optimize the allocation of resources within the system. KGs can also be used to support interoperability between different components or systems, by providing a common framework for communication and data exchange ^[8].

Another usage of KGs is to support predictive maintenance, where the system can use the data collected from sensors and other sources to predict when maintenance may be required for a particular component. This can help to reduce downtime and increase the overall efficiency of the system. Additionally, KGs can be used to support traceability and quality control by tracking the movement of components and materials throughout the production process and recording any quality issues that may arise ^[27].

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Soheil Jaryani

Ibrahim Yitmen

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Sepehr Alizadehsalehi

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Update Date: 22 Sep 2023

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