Autor: Jan Sender

Monitoring construction progress, as required in the one-off production of large structures, is very time- and labor-intensive due to a high level of complexity and individuality. The goal of this article is to develop a sensor-based approach for capturing and evaluating multiple inspection characteristics. The use of machine learning models to detect objects and derive relevant information forms the basis for linking current condition to construction schedule. This enables a significant increase in efficiency during construction progress monitoring and a well-founded assessment of progress.

In engineer-to-order production systems, design and production processes are often carried out simultaneously to achieve shorter throughput times. Shipbuilding frequently adopts this approach. In practice, whilst this may lead to time savings, it can also result in efficiency losses. This article analyzes the causes of these inefficiencies and, as a counteractive measure, develops digital assistance systems for integration in the shipbuilding process chain. Digital assistance systems are based on a digital shadow of the shipbuilding process.

A newly developed hand guided collaborative robot system will be used for manual mechanical joining process in the final assembly of aircrafts. The tool can be moved quickly and precisely to reach all joining positions avoiding physical effort for the operator. Special focus was given on the integrated handling of the entire system. The interlinked sensory of all subsystems ensures a smart control of the system. A mobile device was implemented to increase the usability and to foster the employees’ acceptance of the solution. It enables significantly improved process documentation, reproducibility and transparency.