GKN (Sweden)

The length-scale dependence of fatigue crack growth is evaluated for a set of metallic materials, namely titanium Ti-6Al-4V, ductile iron EN-GJS-500-7 and tool steel AISI H13, by performing fatigue crack growth tests on geometrically similar compact C(T) specimens of different sizes. With references to length-scale-invariant variables, notably the crack growth rate normalised by the specimen width, it is demonstrated that fatigue crack growth is not a length-scale-invariant process for the tested conditions. In particular, the length-scale dependence is less significant for larger specimens and at longer crack lengths. The test results also contradict the hypothesis of similitude, i.e., that the growth rate is uniquely related to the stress-intensity-factor range, as smaller specimens manifest a higher growth rate when compared at the same stress-intensity-factor range. The observations are in line with presented fracture-mechanical demonstrations, which show that the Paris–Erdogan law depends on the length scale whenever fatigue crack growth is anticipated to be scale invariant.

Composites with 3D-textile reinforcement present several engineering advantages. However, their intricate yarn architecture also creates a material with a number of nonlinear behaviours and features, which need to be understood in order to enable their efficient use. To demonstrate the anisotropic development of such non-linear behaviours, and how they depend on loading mode, tensile samples of a 3D-woven layer-to-layer angle interlock carbon-fibre reinforced epoxy composite are tested experimentally (data shared publicly). More specifically, specimens are cut and tested at orientations of 0°, 15°, 30°, 45° and 90° relative to the direction of the warp yarns. The samples are tested cyclically by loading and unloading them at progressively higher displacement values. By monitoring the reduction in stiffness and the development of permanent strains it is possible to identify material parameter values used to calibrate an anisotropic macroscale elasto-plastic damage model. The model shows promising agreement with the experimental results.

This work represents a preliminary study of atmospheric plasma-sprayed (APS) Yttria-Stabilized Zirconia (YSZ)-based thermal barrier coatings (TBCs) deposited on forged and additive manufactured (AM) HAYNES®282® (H282) superalloy substrates. The effect of different feedstock morphologies and spray gun designs with radial and axial injection on APS-deposited YSZ layer characteristics such as microstructure, porosity content, roughness, etc., has been investigated. The performance of TBCs in terms of thermal cycling fatigue (TCF) lifetime and erosion behaviour were also comprehensively investigated. In view of the high surface roughness of as-built AM surfaces compared to forged substrates, two different types of NiCoCrAlY bond coats were examined: one involved high-velocity air fuel (HVAF) spraying of a finer powder, and the other involved APS deposition of a coarser feedstock. Despite the process and feedstock differences, the above two routes yielded comparable bond coat surface roughness on both types of substrates. Variation in porosity level in the APS topcoat was observed when deposited using different YSZ feedstock powders employing axial or radial injection. However, the resultant TBCs on AM-derived substrates were observed to possess similar microstructures and functional properties as TBCs deposited on reference (forged) substrates for any given YSZ deposition process and feedstock.

Abstract In contrast to the highly optimized rotating components, the inter compressor duct (ICD) is offering a high potential for further improvements. Due to a more aggressive design, the length of the whole engine can be reduced. Shorter and thus lighter engines are leading to further fuel savings for the next engine generation. The design of current ICDs is very conservative because of high uncertainties in design space. An extensive test campaign on two highly aggressive ICDs has been conducted at an annular cascade at German Aerospace Center (DLR) Cologne to explore these limitations. The test section consists of low-pressure compressor (LPC)-OGVs, struts, and high-pressure compressor (HPC)-inlet guide vanes (IGVs). To simulate the influence of the last rotor of the LPC and to vary the incidence of the OGV, a moveable swirler is placed in front of the OGV. This simplification results in differences compared to a real rotor outlet flow. Due to geometric constraints, the ability to move the swirler can only be achieved by adding partial gaps in the rear part of the swirler at hub and shroud. These gaps have a huge impact on the secondary flow structure of the swirler outflow and therefore on the inflow of the ICD. In this article, the secondary flow system of an aggressive ICD is analyzed in detail by the means of experimentally validated computational fluid dynamics (CFD) simulations. Special attention is given to the influence of the nonrotating swirler on the secondary flow system. Furthermore, a recommendation for future experimental setups with respect to the described effects is concluded.

The ratio between blade height and chord, named the aspect ratio (AR), plays an important role in compressor aerodynamic design. Once selected, it influences stage performance, blade losses and the stage stability margin. The choice of the design AR involves both aerodynamic and mechanical considerations, and an aim is frequently to achieve the desired operating range while maximizing efficiency. For a fixed set of aerodynamic and geometric parameters, there will be an optimal choice of AR that achieves a maximum efficiency. However, for a state-of-the-art aero-engine design, optimality means multi-objective optimality, that is, reaching the highest possible efficiency for a number of operating points while achieving a sufficient stability margin. To this end, the influence of the AR on the performance of the first rotor row of a multistage, multi-objective, high-speed compressor design is analyzed. A careful setup of the high-speed aerodynamic design problem allows the effect of the AR to be isolated. Close to the optimal AR, only a modest efficiency variation is observed, but a considerable change in compressor stability margin (SM) is noted. Decreasing the AR allows for increasing efficiency, but at the expense of a reduced surge margin. This allows the designer to trade efficiency for stability. Increasing the AR, however, is shown to reduce both the surge margin and efficiency; hence, a distinct optimality in stability is observed for the analyzed rotor blade row. In this work, optimality in the surge margin with respect to the AR is observed, whereas there is a close to optimal efficiency. The predicted range from AR = 1.10 to AR = 1.64 is only indicative, considering that the definition of multi-objective optimality requires balancing efficiency and the surge margin and that the choice of balancing these two criteria requires making a design choice along a pareto optimal front.

Abstract Evaluation approaches are needed to ensure the development of effective design support. These approaches help developers ensure that their design support possesses the general design support characteristics necessary to enable designers to achieve their desired outcomes. Consequently, evaluating design support based on these characteristics ensures that the design support fulfils its intended purpose. This work reviews design support definitions and identifies and describes 11 design support characteristics. The characteristics are applied to evaluate a proposed design support that uses additive manufacturing (AM) design artefacts (AMDAs) to explore design uncertainties. Product-specific design artefacts were designed and tested to investigate buildability limits and the relationship between surface roughness and fatigue performance of a design feature in a space industry component. The AMDA approach aided the investigation of design uncertainties, identified design solution constraints, and uncovered previously unknown uncertainties. However, the results provided by product-specific artefacts depend on how well the user frames their problem and understands their AM process and product. Hence, iterations can be required. Based on the evaluation of the AMDA process, setting test evaluation criteria is recommended, and the AMDA method is proposed.

Presented is a system to digitalize, assist and automate visual inspection of aerospace component surfaces. Industrial robotic and camera equipment are applied for image acquisition, while deep learning models are used for automated defect detection. Operator interaction for evaluation of indications is handled in a user interface developed for this purpose. Inspection data is stored in a database, allowing to trace back results and continuously improve the system. The system is designed as a platform for different sensors and different inspection processes. The conducted development contributes to increase efficiency, effectivity and traceability of inspection processes in the aerospace industry.

Abstract A radical shift in technology is necessary to enable future air transport solutions. Sustainability targets for aeroengine manufacturing mean more than reducing CO2 and NOX. The future will open up possibilities and bring new challenges when introducing hybrid- and electrical propulsion technologies using new materials, technology solutions and new business models. This article reports on findings from a longitudinal study and many years of collaboration between researchers and industry experts, where a first-tier aeroengine manufacturer transforms their product development capabilities to enable sustainable product development. The article highlights some activities undertaken and identifies critical challenges and opportunities remaining for a manufacturer of next-generation aeroengine solutions. It is argued that the challenge for aeroengine manufacturers to develop new-generation propulsive technologies will require a systemic change in the undertaking of design and development. The opportunities of sustainable technologies are evident yet require: (1) means to tighter integrate business and technology development, (2) the ability to quantify and assess sustainability impacts of different concept solutions, and (3) means to utilise natural resources, alloys and materials for a circular and life-cycle optimised solution.

Large amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which result in the class-imbalance issue. Using imbalanced datasets, classifiers often provide sub-optimal classification results, i.e. better performance on the majority class than the minority class. However, it is important for process engineers that models classify defects more accurately than the class with no defects since this is crucial for quality inspection. Hence, we address the class-imbalance issue in manufacturing process data to support in-situ quality control of additive manufactured components. For this, we propose cluster-based adaptive data augmentation (CADA) for oversampling to address the class-imbalance problem. Quantitative experiments are conducted to evaluate the performance of the proposed method and to compare with other selected oversampling methods using AM datasets from an aerospace industry and a publicly available casting manufacturing dataset. The results show that CADA outperformed random oversampling and the SMOTE method and is similar to random data augmentation and cluster-based oversampling. Furthermore, the results of the statistical significance test show that there is a significant difference between the studied methods. As such, the CADA method can be considered as an alternative method for oversampling to improve the performance of models on the minority class.

This paper aims to address data labelling issues in process data to support in-situ process monitoring of additive manufactured components. For this, we adopted an active learning (AL) approach to minimise the manual effort for data labelling for classification models. In this study, we present an approach that utilises pre-trained models to extract deep features from images, and clustering and query by committee sampling to select the representative samples to build defect classification models. We conduct quantitative experiments to evaluate the proposed method’s performance and compare it with other selected state-of-the-art AL approaches using a dataset of additive manufacturing (AM) and a publicly available dataset. The experimental results show that the proposed approach outperforms AL with committee based sampling, and AL with clustering and random sampling. The results of the statistical significance test show that there is a significant difference between the studied AL approaches. Hence, the proposed AL approach can be considered an alternative method to reduce labelling costs when building defects classification models, whose generalizability is most likely plausible.