Machine vision solutions can perform within a wide range of applications and are commonly used to verify the operation of production systems. They offer the potential to automatically record assembly states and derive information, but simultaneously require a high effort of planning, configuration and implementation. This generally leads to an iterative, expert based implementation with long process times and sets major barriers for many companies. Furthermore the implementation is task specific and needs to be repeated with every variation of product, environment or process. Therefore a novel concept of a simulation-based process chain for both—configuration and enablement—of machine vision systems is presented in this paper. It combines related work of sensor planning algorithms with new methods of training data generation and detailed task specific analysis for assembly applications.
Highest demands for complete traceability and quality control of each component, require thorough identification of each produced, replaced, and (dis-)assembled aircraft component. As many production and MRO-processes for modern aircraft remain to be carried out manually, this poses a great challenge. Many small components either do not feature a Part Number or in MRO-processes their Part Number is occluded or not readable due to dirt and wear. Considering unmarked components with a high resemblance to one another and few characteristics, e.g. standard parts such as bushings and pipes, manual identification is an error-prone task. Avoiding errors through digitalized procedures has the potential to significantly reduce error rates and costs for a typical manual dual control. However, automated identification of components has to overcome the high classification complexity that originates in the manifold of aircraft components and is additionally increased by individualistic MRO modifications for specific aircraft. This work presents a methodological approach to reveal possible challenges for identification procedures and gives special focus to the assessment of similarities between components. Two similarity metrics are introduced that are calculated either through feature-based analysis or through 3D-shape similarity assessment. The methodology is demonstrated with two to this date unsolved Use-Cases that represent different challenges of visual identification systems for similar and unmarked components.
<div class="section abstract"><div class="htmlview paragraph">Threaded, potted inserts are commonly used as a standard connecting element for sandwich components, which are used for aircraft interior. Since they often offer the only detachable connection, they are used in very high quantities. To ensure a material bond between the inserts and the honeycomb structure, the joint is filled with adhesive. Despite the high number of inserts, this process is performed manually. Recent research has shown new approaches for automated gripping and placement of the inserts by an industrial robot that yield high potential for cost savings and increased productivity. Automated adhesive insertion, so-called potting, has not been considered so far but is an essential contribution to the full automation of the entire process chain. The amount of adhesive varies depending on the type of insert and its position on the honeycomb structure. During the potting process, it is also mandatory to prevent air inclusions, to fulfill safety requirements in the aviation industry. This paper presents an approach for automated potting to further increase the degree of automation during sandwich panel production. First, the range of insert types is analyzed and the joint geometries are investigated. Based on the derived requirements, an automation concept is introduced and a parameter study is carried out. Optimal parameters are derived which can be used for process control. Finally, the overall process capability is shown and evaluated using a flexible, automated system.</div></div>
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