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Aerodynamic feeding systems represent one possibility to meet the challenges of part feeding for automated production in terms of feeding performance and flexibility. The aerodynamic feeding system investigated in this article is already able to adapt itself to different workpieces using a genetic algorithm. However, due to the operating principle, the system is susceptible to changes in environmental conditions such as air pressure and pollution (e.g. dust). To minimise the effect of ambient influences, the system must be enabled to detect changes in the feeding rate and react autonomously by adapting the system’s adjustment parameters. In this work, based on pre-identified factors interfering with the aerodynamic orientation process, a new approach is developed to react to changes of the ambient conditions during operation. The presented approach makes us of an alternating sequence of monitoring and corrective algorithms. The monitoring algorithm measures the ratio of correctly oriented parts to the total number of fed parts of the process and triggers the corrective algorithm if necessary. Simulated and experimental results both show that an increased feeding rate can be achieved in varying conditions. Furthermore, it is shown that integrating both known process and parameter information can reduce the time for re-parametrisation of the feeding system.

In order to meet the demands for flexible feeding technology, a self-learning aerodynamic part feeding system has been developed. The actuated system uses a genetic algorithm to find the optimal parameter set for a high rate of correctly oriented parts. This orientation rate can change due to changes in the ambient conditions (e.g. ambient pressure, coefficient of friction). When the orientation rate in pre-defined interval of parts drops below a determined value, a correction algorithm is triggered. The objective of this work is to develop a mathematical model to define the optimal control interval and limit of the orientation rate for triggering the corrective algorithm depending on the total amount of parts still to be fed at any point in time. To evaluate the mathematical approach, a macroscopic simulation model of the aerodynamic feeding system was developed. It was shown, that the feeding time of a batch of 10,000 parts can be reduced by up to 7% and the number of activations of the corrective algorithm can be reduced by up to 50%. Finally, the mathematical model was implemented in the system control.

Um bestehenden Defiziten konventioneller Zuführtechnik hinsichtlich Zuführleistung, Zuverlässigkeit und Variantenflexibilität entgegenzuwirken, wurde eine aerodynamische Zuführanlage entwickelt. Die Anpassung dieser Zuführanlage an unterschiedliche Werkstücke erfolgt autonom über die Konfiguration von fünf Anlagenparametern. In diesem Beitrag werden aktuelle Fortschritte zur Steigerung der Variantenflexibilität, Verkürzung der Rüstzeit sowie Erhöhung der Ausbringung im Batchbetrieb präsentiert und Perspektiven für zukünftige Entwicklungen aufgezeigt., To counteract existing deficits of conventional feeding technology with regard to feeding performance, reliability and flexibility, an aerodynamic part feeding system was developed. This feeding system can adapt to different workpieces autonomously by configuring five system parameters. In this article, current advances with regard to an increased flexibility, shortened retooling times and increased output in batch production are presented and perspectives for future developments are given.

Part 2: Assembly Design and Planning; International audience; To meet the demands for flexible assembly technology, an aerodynamic feeding system has been developed. The system autonomously finds the optimal configuration of four parameters – two angles of inclination, nozzle pressure and component speed – using a genetic algorithm, which has been presented in earlier work. To increase the flexibility of the feeding system, an actuator was implemented, that enables the variation of the nozzle position orthogonally to the moving direction of the components. This paper investigates the effects of the more flexible flow against the components on their behavior when passing the nozzle. Additionally, the nozzle position was implemented into the genetic algorithm as a fifth parameter. Therefore, the impact of the enlargement of the solution space of the genetic algorithm due to the implementation of a fifth parameter is investigated in this paper as well.

In previous research, an aerodynamic feeding system was developed, which autonomously adapts to different components by using a genetic algorithm that controls the physical parameters of the system (e.g. angle of inclination, nozzle pressure). The algorithm starts with two individuals with random values, generated within the boundaries of the parameters set by the user. Due to this, the setting time - the time that passes until a satisfactory orientation rate is reached - is hard to predict. The aim of this work is to identify basic interactions of geometric component properties with the physical parameters of the aerodynamic feeding system to determine in which areas of the workspace a satisfactory solution can be expected. By doing so, the initial population of the genetic algorithm can be generated based on certain geometric properties and would therefore no longer be random, presumably reducing setting time.

While joining tolerances, and therefore forces, are known in the assembly process, the determination of disassembly forces is not possible. This is caused by changes of the product properties during the product operation, which has multiple reasons like thermal or mechanical stress on the product. Regarding the planning of disassembly tasks, disassembly times and tools cannot be planned properly. They have to be determined in the process or stay undefined, which can result in damaging of the product. This article shows an approach to describe the necessary disassembly forces without having to investigate the complex physical influences caused by the usage of the product. A solidifying force that has to be overcome in the disassembly process is defined. To make the solidifying force transferable within the huge amounts of individual products, it is split into a usage factor (e.g. hours of operation) and a product specific factor (e.g. geometry of connection), which specify the influences on the joint properties. The transferability of usage factor to product variants is investigated using a FEM-Simulation.