Generative AI in Manufacturing Systems:Reference Framework and Use Cases

This paper aims to enhance the competitive edge of manufacturers through increased efficiency, propelled by digitalization and the implementation of artificial intelligence (AI) applications. A key focus is on Generative AI, a growing topic of discussion in the AI domain. However, a common definition for Generative AI within the context of manufacturing systems is lacking. This paper seeks to establish a clear definition for Generative AI in manufacturing systems, and subsequently structures potential application fields and objectives within a GenAI reference framework. In this context, a framework is proposed to characterize use cases within manufacturing systems, providing crucial guidance for manufacturers looking to leverage Generative AI. The theoretical background explores the definition of manufacturing systems and the intersection between data analytics and AI. Furthermore, the paper discusses varying definitions of Generative AI, and derives a definition suitable for manufacturing systems.</p

Model-based approach for technology assessment in battery manufacturing process chains and factories

The introduction of new technologies in battery manufacturing process chain constitutes a key strategy to boost throughput, lower energy demands and battery costs. Implementing new technologies in the process chain is a costly, time-consuming and resource-intense exercise. Therefore, manufacturers have to prioritize technologies with great potential benefits since there are various alternative technologies being proposed. To cope with such problem, this paper presents a model-based approach to aid decision makers in the pre-assessment of new process technologies and strategies in the battery manufacturing process chain. The multi-factor model considers the cell performance parameters, equipment operating parameters and building environment configurations to ascertain the demands in the manufacturing process chain caused by new technologies. A case study for the production of an NMC333 18650-type battery cell was used to test the model. Energy demand and production cost were assessed in 3 different scenarios (pessimistic, literature based and optimistic), determined by the equipment operating conditions. The results of the model were benchmarked against those reported in the literature and have shown to be comparable. The model has shown to be flexible and user-friendly. Its application extends from gaining quick insights of the manufacturing process chain, screening technologies before embarking in a more detailed assessment and for planning the establishment of a battery manufacturing plant.</p

Ultrasonic-based leak detection in factories with spatial mapping

Energy consumption of compressed air in the factory raises a high cost, while a significant part of energy loss is caused by leakage. Traditional methods involve labor-intensive processes using handhold detectors to detect ultrasound generated by compressed air leakages. However, the best practice requires ongoing leak detection and repair. Crucial to this is the visualization of ultrasonic distribution in the factory based on ultrasound data at various positions. Although various automatic ultrasonic air leak detection methods are applied in small pneumatic systems, there are few methods available for large factory environments. This paper proposes a leak detection method for the factory to identify the leaks at an early stage via spatial mapping. The optimal mapping method is ordinary kriging with a spherical variogram, exhibiting a relative error of 4.7%. In pursuit of practical industrial application, we conducted case studies on the impact of background noise, measuring directivity as well as susceptibility to machine interference. The results proved the applicability of this method for industry to proactively seek for air leaks and reduce energy loss.</p

Circular Manufacturing Systems in Learning Factories

Circular Manufacturing Systems (CMS) have emerged as a paradigm for integrating disassembly and potential remanufacturing processes into factories. This is also a relevant topic for learning factories whereas studies indicate that limited concepts are available nowadays. Given that, the paper explores the integration of CMS principles as well as specific processes and system setups into learning factories. A methodological approach is introduced to facilitate the systematic development of CMS approaches for learning factories. The proposed approach is exemplarily applied in the Learning Factory of the University of Twente to test functionality and feasibility.</p

Manufacturing

The manufacturing industry is responsible for a large share of global environmental impacts (e.g., greenhouse gas emissions) that can mainly be tracked back to energy demand. This energy demand is determined by a diversity of processes and machines, which dynamically interact in process chains and with other factory elements such as technical building services (TBS). Given that, system-oriented material flow simulation with inclusion of energy aspects bears the potential to support the energy transition of industry through fostering both energy efficiency and substitution towards renewable resources. The chapter addresses the necessary background as well as common aspects in the context of energy-oriented manufacturing system simulation. Four manufacturing case studies underline the feasibility and potential of available simulation approaches for improving energy-related environmental impacts and also costs. Additionally, an outlook towards potential future research steps is given.</p