This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress detection in manual assembly via HAR-based methods, as well as visual tool-recognition approaches. A novel online tool-recognition pipeline for handheld tools is introduced, utilizing a two-stage approach. First, a Region Of Interest (ROI) was extracted by determining the wrist position using skeletal data. Afterward, this ROI was cropped, and the tool located within this ROI was classified. This pipeline enabled several algorithms for object recognition and demonstrated the generalizability of our approach. An extensive training dataset for tool-recognition purposes is presented, which was evaluated with two image-classification approaches. An offline pipeline evaluation was performed with twelve tool classes. Additionally, various online tests were conducted covering different aspects of this vision application, such as two assembly scenarios, unknown instances of known classes, as well as challenging backgrounds. The introduced pipeline was competitive with other approaches regarding prediction accuracy, robustness, diversity, extendability/flexibility, and online capability.

Globally interconnecting machines, processes, and resources driven by exploring and advancing new technologies defined Industry 4.0 (I4.0), resulting in, e.g., Cyber-Physical Production Systems (CPPS). The aircraft industry particularly struggled with transforming production and Maintenance, Repair, and Overhaul (MRO) processes, replacing humans with machines and automating as well as digitalizing significant parts of their value- and non-value-adding activities. However, in the face of current social and environmental challenges, future industries will need to shift from purely technology-driven to value-driven, working sustainably with resources, including human capital. Together, these approaches constitute the idea of Industry 5.0 (I5.0). On the one hand, the aviation industry faces the challenge that even I4.0 concepts and technologies are not yet fully exploited or implemented. On the other hand, due to the specific characteristics of aircraft production and MRO as well as the environmental impact of the product, a tremendous potential arises regarding placing human well-being back into the center of adding value and decreasing environmental footprint while building an industry that is resilient and fortified against disruptions of this era. In line with the I5.0 terminology, in this work, we outline the challenges and opportunities of integrating I5.0 principles into the aircraft production and MRO industries, focusing specifically on the scope of selected use cases.

The global push for sustainable energy has heightened the demand for green hydrogen, which is crucial for decarbonizing heavy industry. However, current electrolysis plant capacities are insufficient. This research addresses the challenge through optimizing large-scale electrolysis construction via standardization, modularization, process optimization, and automation. This paper introduces H₂Giga, a project for mass-producing electrolyzers, and HyPLANT100, investigating large-scale electrolysis plant structure and construction processes. Modularizing electrolyzers enhances production efficiency and scalability. The integration of AutomationML facilitates seamless information exchange. A digital twin concept enables simulations, optimizations, and error identification before assembly. While construction site automation provides advantages, tasks like connection technologies and handling cables, tubes, and hoses require pre-assembly. This study identifies key tasks suitable for automation and estimating required components. The Enapter Multicore electrolyzer serves as a case study, showcasing robotic technology for tube fittings. In conclusion, this research underscores the significance of standardization, modularization, and automation in boosting the electrolysis production capacity for green hydrogen, contributing to ongoing efforts in decarbonizing the industrial sector and advancing the global energy transition.

This paper introduces a novel Human Action Recognition (HAR) dataset designed to improve Human-Robot Collaboration (HRC) in green electrolyzer production. Recorded in a lab using RGB, depth, and skeletal data from Azure Kinect, the dataset focuses on assembly tasks, labeled with Methods-Time Measurement (MTM) primitives. The use of a green screen enables the study of background effects on HAR algorithms. The dataset addresses the challenges of data imbalance and limited training data in industrial HAR applications, offering standardized, mergeable, and extendable data for the research community. It aims to enhance the development of HAR algorithms in manufacturing contexts, with future plans for collaborative expansion and real-world application. The dataset and further information are available via a GitHub repository.