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Thermal properties and calcium-magnesium-alumino-silicate (CMAS) interaction of novel γ-phase ytterbium-doped yttrium disilicate (γ-Y1.5Yb0.5Si2O7) environmental barrier coating material
No full textPublisher Copyright: © The Author(s) 2024.Rare-earth disilicates are promising candidates for thermal and environmental barrier coatings (TEBC) in gas turbines that safeguard SiCf/SiC ceramic matrix composites (CMCs) from thermal degradation and environmental attacks. Here, we report a systematic investigation on novel TEBC material, γ-Y1.5Yb0.5Si2O7. The γ-phase quarter molar ytterbium–doped yttrium disilicate exhibited low thermal conductivity (1.72 W·m−1·K−1 at 1200 °C) and reduced intrinsic thermal expansion (3.17 ± 0.22 × 10−6 K−1 up to 1000 °C), ensuring promisingly effective thermal insulation and minimized thermal stress with CMC substrates. Using density functional theory (DFT), the heat capacity of γ-Y1.5Yb0.5Si2O7 was predicted higher than that of undoped γ-Y2Si2O7. Comparing these predictions to results calculated using the Neumann–Kopp (NK) rule revealed only minor variations. A metastable CMAS interaction byproduct, cyclosilicate phase Ca3RE2(Si3O9)2, was identified based on energy dispersive X-ray spectrometer (EDS) and electron backscatter diffraction (EBSD) techniques, appearing at 1300 °C but disappearing at 1400 °C. The γ-Y1.5Yb0.5Si2O7 exhibited good CMAS resistance on both dense pellets and sprayed coatings, forming a protective apatite (Ca2RE8(SiO4)6O2) interlayer that effectively hindered CMAS infiltration at evaluated temperatures. The relatively higher Y:Yb atomic ratio (> 3) in the apatite grains indicate differential reactivity with molten CMAS and provides crucial insights into the CMAS corrosion mechanism. These findings highlight the potential of γ-Y1.5Yb0.5Si2O7 as a CMC coating material, emphasizing the need for tailored microstructural optimization as a thermal sprayed coating to enhance long-term performance in extreme gas turbine environments.Peer reviewe
Influence of Crystallographic Structure and Metal Vacancies on the Oxygen Evolution Reaction Performance of Ni-based Layered Hydroxides**
No full textPublisher Copyright: © 2023 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.Nickel-based layered hydroxides (LHs) are a family of efficient electrocatalysts for the alkaline oxygen evolution reaction (OER). Nevertheless, fundamental aspects such as the influence of the crystalline structure and the role of lattice distortion of the catalytic sites remain poorly understood and typically muddled. Herein, we carried out a comprehensive investigation on ɑ-LH, β-LH and layered double hydroxide (LDH) phases by means of structural, spectroscopical, in-silico and electrochemical studies, which suggest the key aspect exerted by Ni-vacancies in the ɑ-LH structure. Density functional theory (DFT) calculations and X-ray absorption spectroscopy (XAS) confirm that the presence of Ni-vacancies produces acute distortions of the electroactive Ni sites (reflected as the shortening of the Ni−O distances and changes in the O−Ni−O angles), triggering the appearance of Ni localised electronic states on the Fermi level, reducing the Egap, and consequently, increasing the reactivity of the electroactive sites in the ɑ-LH structure. Furthermore, post-mortem Raman and XAS measurements unveil its transformation into a highly reactive oxyhydroxide-like phase that remains stable under ambient conditions. Hence, this work pinpoints the critical role of the crystalline structure as well as the electronic properties of LH structures on their inherent electrochemical reactivity towards OER catalysis. We envision Ni-based ɑ-LH as a perfect platform for hosting trivalent cations, closing the gap toward the next generation of benchmark efficient earth-abundant electrocatalysts.Peer reviewe
Absorption and birefringence study for reduced optical losses in diamond with high nitrogen-vacancy concentration
Get PDFPublisher Copyright: © 2023 The Authors.The use of diamond colour centres such as the nitrogen-vacancy (NV) centre is increasingly enabling quantum sensing and computing applications. Novel concepts like cavity coupling and readout, laser-threshold magnetometry and multi-pass geometries allow significantly improved sensitivity and performance via increased signals and strong light fields. Enabling material properties for these techniques and their further improvements are low optical material losses via optical absorption of signal light and low birefringence. Here, we study systematically the behaviour of absorption around 700 nm and birefringence with increasing nitrogen- and NV-doping, as well as their behaviour during NV creation via diamond growth, electron beam irradiation and annealing treatments. Absorption correlates with increased nitrogen doping yet substitutional nitrogen does not seem to be the direct absorber. Birefringence reduces with increasing nitrogen doping. We identify multiple crystal defect concentrations via absorption spectroscopy and their changes during the material processing steps and thus identify potential causes of absorption and birefringence as well as strategies to fabricate chemical vapour deposition diamonds with high NV density yet low absorption and low birefringence. This article is part of the Theo Murphy meeting issue 'Diamond for quantum applications'.T.L. and J.J. acknowledge the funding by the German Federal Ministry for Education and Research Bundesministerium für Bildung und Forschung (BMBF) under grant nos. 13XP5063 and 13N16485. AcknowledgementsPeer reviewe
A novel approach for the detection of anomalous energy consumption patterns in industrial cyber-physical systems
No full textPublisher Copyright: © 2022 John Wiley & Sons Ltd.Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.This work has received funding support from the SPRI-Basque Government through the ELKARTEK program (3KIA project, ref. KK-2020/00049). Javier Del Ser also acknowledges support from the Consolidated Research Group MATHMODE (IT1294-19), granted by the Department of Education of the Basque Government. The authors would like to also acknowledge the help and support of GESTAMP (a multinational company dedicated to the design, development and manufacturing of automotive components), which has provided the dataset used in this work and successfully validated the approach described in this article in several of its industrial factories. This work has received funding support from the SPRI‐Basque Government through the ELKARTEK program (3KIA project, ref. KK‐2020/00049). Javier Del Ser also acknowledges support from the Consolidated Research Group MATHMODE (IT1294‐19), granted by the Department of Education of the Basque Government. The authors would like to also acknowledge the help and support of GESTAMP (a multinational company dedicated to the design, development and manufacturing of automotive components), which has provided the dataset used in this work and successfully validated the approach described in this article in several of its industrial factories.Peer reviewe
Robots Adapting to the Environment: A Review on the Fusion of Dynamic Movement Primitives and Artificial Potential Fields
Get PDFPublisher Copyright: © 2013 IEEE.For the development of autonomous robotic systems, Dynamic Movement Primitives (DMP) and Artificial Potential Fields (APF) are two well known techniques. DMPs are a reference algorithm in robotics for one shot learning as they enable learning complex movements and generating smooth trajectories, while APF are outstanding in navigation and obstacle avoidance tasks. By integrating DMPs and APF, the task automation capability can be significantly enhanced, as the precision of DMPs combined with the reactive nature of APF promises, in theory, adaptability and efficiency for the learning algorithm. Despite the numerous papers discussing and reviewing both techniques independently, there is a lack of an objective comparison of the investigations combining both approaches. This paper aims to provide such a comprehensive literature analysis, using a homogenized mathematical formulation. Moreover, a categorization based on their application scope, the robots used and their characteristics is provided. Finally, open challenges in the combination of DMP and APF are discussed, highlighting further works that are worth conducting for improving the integration of both approaches.Peer reviewe
Unsupervised Machine Learning for Blind Rivets Quality Inspection
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Fastening plays a crucial role in aircraft manufacturing, and the demand for automated solutions has grown. Blind rivets are appealing for automation but require indirect assessment of the formed head for quality monitoring. Unsupervised machine learning holds potential for blind rivet inspection and extends to industrial data clustering/classification. In this context, labeling industrial data is challenging due to production focus and the need for NO OK labels. Unsupervised machine learning and advanced data analysis methods offer opportunities to optimize quality control processes without manual labeling or costly experiments. This paper proposes two approaches to address the issue by clustering time-dependent signals in the riveting process. After preprocessing the signals, different clustering techniques are applied to time-series and signal features to obtain OK and NO OK installation clusters. The first approach, using Euclidean distance and Dynamic Time Warping, yields poor clustering results. The second approach involves feature extraction using time domain and expert descriptors, along with dimensional reduction techniques (PCA, UMAP), followed by clustering techniques. UMAP combined with DBSCAN clustering achieves interesting results, with high precision and accuracy values (above 0.8) for both OK and NO OK clusters.Peer reviewe
Innovative Tooling Solutions for Aircraft Assembly: A Case Study on the Development of Reconfigurable and Lightweight Fixtures
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.This paper presents a case study on the development of reconfigurable and lightweight fixtures for aircraft assembly, which are essential for modernizing and automating tooling fixtures in the aeronautical industry. Our study utilizes generative engineering and other design techniques to optimize performance and meet design objectives. Generative engineering combines parametric and algorithmic design with mathematical optimization, while generative design utilizes algorithmic methods to generate feasible designs based on performance goals, constraints, and design space. To determine the most appropriate optimization tools, we conducted a thorough study of available options and selected the most suitable ones based on available resources. Our newly developed fixtures are flexible and adaptable to various types of aircraft structures, including fuselages and wings, and meet crucial vibration requirements for aircraft assembly operations such as drilling and riveting. The new fixtures are also lightweight and easier to handle, contributing to a reduction in development costs and enhancing production efficiency. This contribution can help to enhance the competitiveness of the Basque aeronautical industry and position it as a key player in the global aviation market. The findings of this study demonstrate the benefits of incorporating generative engineering and novel design techniques into the development of innovative tooling solutions for aircraft assembly.Peer reviewe
Scalable Data Profiling for Quality Analytics Extraction
No full textPublisher Copyright: © IFIP International Federation for Information Processing 2024.In today’s modern society, data play an integral role in the development global industry, since they have become a valuable asset for companies, institutions, governments, and others. At the same time, data generated daily, at a global scale, require significant resources to pre-process, filter and store. When it comes to acquiring such stored data, it is essential to understand which dataset fits to the needs of the user beforehand. One particularly important factor is the quality of a dataset, which could be determined based on a series of quality related attributes generated by it. Such attributes constitute “Profiling”, the process of obtaining information from a data sample, related to the complete dataset’s quality. However, in the era of Big Data, the ability to apply profiling techniques in complete large datasets should also be considered, in order to obtain complete quality insights. This paper attempts to provide a solution for this consideration by presenting “DaQuE”, a scalable framework for efficient profiling and quality analytics extraction in complete datasets of all volumes.Peer reviewe
Control co-design for wave energy farms: Optimisation of array layout and mooring configuration in a realistic wave climate
Get PDFPublisher Copyright: © 2024 Elsevier LtdThis paper presents a novel Control Co-Design (CCD) methodology aimed at economically optimising the layout of wave energy converter (WEC) arrays. CCD ensures the synergy of optimised WEC and array parameters with the final control strategy, resulting in a comprehensive and efficient design of the array. By integrating a spectral-based control strategy into the array layout design, this study pursues the twin objectives of maximising energy absorption while reducing costs. To prove the performance of the proposed CCD methodology, an application case is proposed where the inter-device distance, alignment, and mooring configuration of a five-device array, considering realistic wave scenarios, are optimised. Energy capture and system cost evaluations are conducted, with results emphasising the significance of incorporating advanced control strategies in the design phase to improve energy absorption and reduce costs. With the application case, the study demonstrates that the optimal layout of a WEC array considering economic factors may differ from the optimal from purely technical factors, such as energy absorption, in the analysed case.Peer reviewe
Optimizing edge-state transfer in a Su-Schrieffer-Heeger chain via hybrid analog-digital strategies
No full textPublisher Copyright: © 2024 American Physical Society.The Su-Schrieffer-Heeger (SSH) chain, which serves as a paradigmatic model for comprehending topological phases and their associated edge states, plays an essential role in advancing our understanding of quantum materials and quantum information processing and technology. In this paper, we introduce a hybrid analog-digital protocol designed for the nonadiabatic yet high-fidelity transfer of edge states in an SSH chain, featuring two sublattices, A and B. The core of our approach lies in harnessing the approximate time-dependent counterdiabatic (CD) interaction, derived from adiabatic gauge potentials. However, to enhance transfer fidelity, particularly in long-distance chains, higher-order nested commutators become crucial. To simplify the experimental implementation and navigate computational complexities, we identify the next-to-nearest-neighbor hopping terms between sublattice A sites as dominant CD driving and further optimize them by using variational quantum circuits. Through digital quantum simulation, our protocol showcases the capability to achieve rapid and robust solutions, even in the presence of disorder. This analog-digital transfer protocol, an extension of quantum control methodology, establishes a robust framework for edge-state transfer. Importantly, the optimal CD driving identified can be seamlessly implemented across various quantum registers, highlighting the versatility of our approach.Peer reviewe