This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI).

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI). The Editors Prof. Dr.-Ing. Thorsten Schüppstuhl is head of the Institute of Aircraft Production Technology (IFPT) at the Hamburg University of Technology. Prof. Dr.-Ing. Kirsten Tracht is head of the Bremen Institute for Mechanical Engineering (bime) at the University of Bremen. Prof. Dr.-Ing. Annika Raatz is head of the Institute of Assembly Technology (match) at the Leibniz University Hannover.

This book includes state-of-the-art and original research contributions from two well-established conferences, which collectively focus on the joint design, development, and management of products, advanced production systems, and business for sustainable customization and personalization. The book includes wide range of topics within these subjects, ranging from industrial success factors to original contributions within the field. The authors represent worldwide leading research institutions.

Proceedings of the 10th International Conference on Human Interaction and Emerging Technologies, IHIET 2023, August 22-24, 2023, Université Côte d'Azur, Nice, France.

This book presents peer-reviewed papers from 14th International Conference on Learning Factories (CLF 2024) that took place from April 17-19, 2024, at the University of Twente, the Netherlands. CLF 2024 continued the successful CLF conference series targeting the latest research and development in the field of learning factories. The book is organized into two volumes and covers state-of-the-art research insights towards Learning Factories of the Future including learning factory design, Industry 5.0, digital twinning and VR/AR, 5G/6G in learning factories, AI for manufacturing systems, human-centred work design, human-robot collaboration, sustainability in learning factories, as well as cross-learning factory product/production systems. The book seamlessly integrates theory with real-world practice, empowering learners such as students, qualified engineers, and workers to keep pace with rapidly evolving technologies and methodologies, through enhancing learning factories. It also helps society and industry effectively manage future transitions with addressing current topics around digitalization, sustainability, and lifelong learning in industry.

In today’s fast-paced industrial landscape, the drive for greater efficiency and flexibility in product development has sparked significant interest in innovative automation technologies. This thesis explores the usefulness of various automation techniques for customized products such as Knowledge-Based Engineering (KBE), Multidisciplinary Optimization (MDO) and machine learning frameworks. The research begins by establishing an automated framework for fixture design, combining design automation and MDO to streamline the design process. It then moves to optimizing gas turbines, introducing an automation framework that merges CAD templates with KBE principles. For complex and unstructured production, this thesis explores the use of Reinforcement Learning (RL) to tackle challenges in unstructured manufacturing. By utilizing lightweight physics-based engines and RL, the research advances automated assembly validation and mobile robot operations, pushing the boundaries of adaptive production automation. Furthermore, a framework is developed, which integrates smoothly with industrial robotic platforms showcases practical automation solutions and highlights the adaptability and applicability of digital twin technology in real-world situations. This thesis contributes to the field of product development by providing innovative solutions that are rooted in multidisciplinary research. It bridges the theoretical and practical aspects of automation with solutions that overcomes the obstacles to realize seamless integration between digital and physical realities in a manufacturing context.