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Item type:Publication, Access status: without files , Accuracy Analysis for a Flow Line Process using a Mobile Holding Fixture for Machining CRFP Components (SAE Paper 2022-01-0041)The aerospace sector is challenged to produce airplanes more efficiently and resiliently in the future. This leads to an increasing demand for improving productivity and flexibility as well as providing solutions for sustainable developments. A bottleneck in production is the machining of large-scale components. Apart from the machining tasks, non-productive operations like fixture adjustment, component handling, referencing and localization are performed within the machining station and can constitute up to 50% of the overall workload. In the UniFix project, Fraunhofer IFAM is participating in the development of a mobile fixture system for large-scale aircraft components, like vertical tail plane and landing flap components of the single aisle aircrafts. By installing components into a mobile holding fixture with an according referencing scheme, a flow line can be established that is composed of specialized workstations discharging the machining station from non-productive processes, leading to higher productivity. This paper focusses on the component localization and referencing strategies for this mobile holding fixture. Here, specific challenges are caused by the transport in between the referencing and the machining process. Therefore, influences that contribute to the overall geometric uncertainty of the referencing process for machining are evaluated in detail. In this work, an automated laser scanner based localization of the components will be presented. Furthermore, estimations on the uncertainty in the involved systems will be conducted and propagated through the localization process.Publicationtype: Conference Paper70 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards an Intelligent Digital Cabin Twin to Support an Aircraft's Retrofit and Base Maintenance (SAE Paper 2022-01-0046)Aircraft are high value-adding and long-living assets, while aircraft cabins are expensive consumer products tailored to each customer. Vastly changing requirements and needs force aircraft holders regularly to instruct modifications in order to remain attractive on the market. Adaptations, modifications, and development of innovations are handled by multiple organizations, not by a central one like the aircraft's manufacturer or owner. Although the Continuing Airworthiness Management Organization manages all aircraft instance-specific documents as required by aviation regulations, their format and types of management differ. Besides, not all information that arises during a parts design phase is included. That means, overall, the consistent model-based maintenance of data within all phases of PLM up to disposal is not guaranteed. The loss of information during all lifecycle phases can cause the prolongation of planning phases and the actual ground-time during retrofit processes since further data acquisitions, processing, and linkage to determine an as-is state are necessary. Employing a Digital Twin can minimize the effort to accumulate, manage, and provide the consistent model-based cabin's as-is state. This work proposes an integrated concept to maintain and associate 3D/2D as-is, as-designed, descriptive, as-well-as document-based data that form a digital representation of the real aircraft's cabin and airframe structure it is interfacing. Storing and providing the data as a digital replica does not create substantial added value yet. Therefore, we present a concept that qualifies the Digital Twin's intelligent part through an automatic generation of descriptive data and semantics. Eventually, we propose valuable applications that will significantly enhance planning phases based on the twin's output.Publicationtype: Conference Paper146 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Approach for digitalization of maintenance processes within the aircraft cabinEnhancing the comfort for passengers, airlines are constantly increasing the number of services within the aircraft cabin such as meal ordering directly from passenger seats. The payment and menu selection can be completely processed by means of a passenger-individual airline user account also considering the remaining inventory of the galley. The implementation of such type of services is supported by digitalization of cabin business processes. For these new services the airline requires a high availability of the process-related system functions to ensure airline’s revenue and customer satisfaction. A possible approach to reach the target of high availability of these functions is to use the trend of digitalization for improving function-relevant maintenance processes within the aircraft cabin. This requires an introduction of flexible communication architectures to enhance the existing maintenance process by establishing an automated fault detection, root cause analysis, and/or even fault prediction. For implementation it is necessary to provide system data and parameters digitally, e.g. by sensor measurements or an online evaluation of already existing system data logs. An example is the working light within the aircraft galley used to create an appropriate working environment for the cabin crew to prepare ordered meals for the passengers. Currently, fault detection is only possible by identifying its failure effect, i.e. a not illuminating lighting unit. However, by measuring the current consumption of the light source via a digital sensor, it would be possible to detect a changing trend in the current consumption and, in turn, triggering a maintenance task to replace the light before it actually fails. The evaluation of such current measurement with the resulting fault prediction would expand the maintenance system by a new function. This paper describes how digitally supportable maintenance processes for any cabin system can be systematically identified and which system modifications are necessary for realization. Further-more, a methodology for cost and benefit assessment is introduced to find the right level of digitization for the cabin system under analysis.Publicationtype: Conference Paper140 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Tool Wear Classification in Automated Drilling Operations of Aircraft Structure Components using Artificial Intelligence Methods (SAE Paper 2022-01-0040)(2022-03-08); ; ; Structural components in fuselage barrels are joined with the help of riveting processes. Concerning the key feature of rivet drill hole size and drilling quality, a poorly executed drilling operation can lead to serious riveting defects such as rivet play or fracture due to non-uniform load distribution. Consequently, the drilling process of a rivet hole and its correct execution is of vast importance for the airworthiness of an aircraft. The condition of the drill used, i.e., the current tool wear, has a direct effect on the quality of the hole. Since conventional approaches, such as changing the tool after a predefined number of process cycles, do not reflect real tool wear, premature wear may occur, resulting in defects. Thus, the online-detection of tool wear for necessitated replacement may indicate a promising future direction in quality control. Since the aircraft industry has a particularly high requirement for defect-free production of structural components, this paper presents a study on the online-detection of tool wear in automated drilling processes using a combination of external sensor technology and Artificial Intelligence methods. For this reason, a laboratory setup to conduct automatic drilling operations in fuselage material is introduced. Two sensor types are utilized to capture the process data that is evaluated by machine learning algorithms. The performance of different machine learning algorithms is measured, and recommendations for action in sensor solutions, and the respective choice of algorithms for this task, are derived. Finally, the results of the study are discussed, and recourse for future work is elaborated upon.Publicationtype: Conference Paper132