AI2SB: Integrating AI into Small Batch Manufacturing: Enhancing Customization and Efficiency through Digital Twins, Additive Manufacturing and Smart Assembly

• Applicant: Professor Dr.-Ing. Benjamin Schleich
• Subject Area: Production Automation and Assembly Technology ; Engineering Design, Machine Elements, Product Development
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 547432938

The use of Artificial Intelligence (AI) and Digital Twins (DT) in engineering can help counter the greater flexibility required in production systems with value creation networks on one hand and ongoing price pressure from global competitors on the other. However, due to the high implementation costs, these systems are typically only used in rigid large-scale production. However, additive manufacturing (AM) systems offer great potential for increasing process efficiency, particularly in small series production that requires high levels of customization. AM is a flexible manufacturing process that is increasingly used for producing highly individualized products, such as spare parts or prototypes, due to its flexibility. To date, the life cycle of highly individualized components has not fully considered the potential of AI and DT. This project focuses on the repair of highly individualized components as an example process, which represents a significant portion of the production chain. Additionally, it serves as proof of concept for the use of decentralized repair systems. These systems could play a crucial role in the circularity of value chains in product lifecycle management (PLM) in the future. To integrate AI systems into production processes, it is necessary to find suitable applications and validate possible concepts. First, examine the various sub-processes of the repair cycle individually and develop measures to increase efficiency with AI and DT in each case. Then, analyze and optimize the design phase for highly individual assemblies based on the collected information. Concepts and AI tools will be developed to enable more efficient assembly, disassembly, and additive manufacturing of components. The process information is fed back into the design phase, allowing for the next generation of components to be designed more efficiently. The tools used in the design phase serve as a guide for the design of highly individualized components for an AI- and DT-supported repair cycle. Repairing components presents a challenge not only in designing replacement parts but also in disassembling partially damaged and highly individualized components. To address this challenge, assembly with AI tools and DT will be optimized, followed by the development of AI tools specifically for the complex disassembly of individual components. In addition, the project will provide insights into the synergistic implementation of AI and DT in processes.

• DFG Programme: Research Grants
• International Connection: Brazil
• Partner Organisation: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; (CAPES); Setor bancario Norte
• Cooperation Partners: Professor Fred Amorin; Professorin Linda Lee Ho; Professorin Izabel Fernanda Machado; Professor Dr.-Ing. Klaus Schützer; Professor Dr. Eduardo De Senzi Zancul; Dr. Thayla Tavares de Souza Zomer