6558 research outputs found
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Development of back-face coatings for the characterization of non-reflective and opaque materials by laser shocks
No full textThe present study intends to make possible the dynamic characterization of a non-reflective and opaque material using a laser shock test which must lead to the establishment of an equation of state and a constitutive law. The instrumentation used is a back-face velocity measurement by a green laser interferometry (VISAR). In this study,
a metallic coating (aluminum) is added to the back-face of the non-reflective material to be characterized (here an elastomer). The influence of the back-face coating thickness on material characterization is studied experimentally and numerically. The experimental results indicate that, whatever the thickness of the metal coating, it is possible to determine the equation of state of the material using a simulation model. Limitations of the pro posed protocol are then finally discussed to get a constitutive law at loading up to 20 GPa. The role of a probable early damaging at the back-face of the sample and improvements proposals are given through numerical analysis
of the shock tests
Multi-scale magnetic aging model: Precipitation kinetics and magnetic hysteresis coupling
Full text linkOperating temperatures of electrical machines are known to cause various effects on magnetic performances and losses. On the one hand, reversible contributions may reduce iron losses through temporary lowering of conductivity, which in turn reduces eddy-current losses. On the other hand, when specific thermal conditions are met, the time-temperature combination may lead to irreversible changes of the magnetic material properties. This latter phenomenon, the so-called magnetic aging, need to be addressed to further improve energy efficiency, predict and reduce heat-dissipated iron losses. These service conditions indeed lead to changes in the microstructure of the electrical steels. Thus, iron losses suffer from these structural modifications as well as long exposure to constant operating temperatures; magnetic aging results from carbides precipitation and impacts the magnetization of these steels. The present paper hence explores a multi-scale approach, coupling the Johnson-Mehl-Avrami-Kolmogorov (JMAK) precipitation kinetics with a static Jiles-Atherton (J-A) model of magnetic hysteresis for a non-oriented soft ferromagnetic Fe-Si steel. Based on an experimental analysis for temperatures ranging from 160°C to 200°C, the applicability of the chosen approaches will be explored and a parameter-based modeling of magnetic aging will be proposed and proved to be in good agreement with measurement data
Ontology-driven LLM framework for knowledge graph in smart buildings
No full textThe rapid data growth in smart building environments requires advanced tools to integrate, interpret, and utilize this information effectively. Smart buildings generate vast and heterogeneous data streams, including sensor readings, occupancy metrics, and environmental conditions, which are critical for optimizing energy efficiency, enhancing occupant comfort, and enabling predictive maintenance. However, the lack of a structured approach to automatically connect and contextualize these data sources limits the insights that can be derived. To address these challenges, this paper presents a framework for assessing data in a knowledge graph by automatically retrieving entities and establishing relationships from diverse data sources, incorporating metadata standards and time-series data relevant to smart buildings. A standard ontology from the domain is used to drive the experiment, enabling the automatic construction of a semantic graph-based model for a real-world smart building environment. The framework s objective is to ensure information is comprehensible as a preliminary step to intelligent decision-making in data-driven smart buildings, enabling applications like fault detection, performance measurement, and energy auditing. The proposed approach explores the potential of large language models (LLMs) to automate data integration, reducing reliance on experts. This paper addresses existing literature gaps on metadata mapping and lays the groundwork for future advancements in digital twin technologies for smart building applications
Multi-dimensional measurement of mental workload in industrial context: an experiment in the field of helicopter maintenance
Full text linkAssessing mental workload is essential for optimizing the design of complex systems, particularly in aeronautical maintenance, where operators' activities serve as a crucial safety barrier to ensure optimal system safety levels. One of the roles of human factors in maintainability is, therefore, to anticipate maintenance activities and human behavior from the start of the design cycle. This study pursues a dual objective: firstly, to identify relevant for evaluating mental workload in an industrial maintenance environment, and secondly, to determine which of these indicators correlate with performance degradation. Ten participants performed five maintenance tasks of varying complexity on a helicopter, involving the removal, installation of components and a detailed inspection. Subjective measures (NASA-TLX), performance metrics (completion time), and cardiovascular data (heart rate, heart rate variability) were analyzed. We observed longer completion times and higher NASA-TLX scores for complex maintenance conditions. Regarding cardiovascular data, the results in the time domain of heart rate variability follow a similar trend compared to two other types of measurements. These results will be discussed in depth in this article. This study represents a further step in the multidimensional measurement of mental workload in maintenance within a realistic industrial context
U-NET-based deep learning for automated detection of lathe checks in homogeneous wood veneers
Full text linkAutomated detection of lathe checks in wood veneers presents significant challenges due to their variability and the natural properties of wood. This study explores the use of two convolutional neural networks (U-Net architecture) to enhance the precision and efficiency of lathe checks detection in poplar veneers. The approach involves sequential application of two U-Nets: the first for detecting lathe checks through semantic segmentation, and the second for refining these predictions by connecting fragmented lathe checks. Post-processing techniques are applied to denoise the mappings and extract precise lathe check characteristics. The first U-Net demonstrated strong performance in predicting lathe check presence, with precision and recall scores of 0.822 and 0.835, respectively. The second U-Net refined predictions by linking disjointed segments, improving the overall lathe checks mapping process. Comparative analysis with manual methods revealed comparable or superior performance of the automated approach, especially for shallow lathe checks. The results highlight the potential of the proposed method for efficient and reliable lathe check detection in wood veneers
Optimizing CrAlN coatings: Effects of deposition temperature on mechanical, tribological, and wettability properties
No full textWith the aim of improving the lifespan of different steel tools by reducing their degradation, CrAlN nitride coatings were investigated. The CrAlN coatings were deposited on X38CrMoV5 steel substrates using the DC reactive magnetron sputtering process under various deposition temperatures between ambient temperature and 300 ◦C. The effects of deposition temperature were systematically explored: XRD, EDS, SEM, optical profilometer, contact angles, nanoindentation, and tribometry were carried out to establish the structural, mechanotribological, and wetting properties’ relationship. Results show that the high deposition temperature promotes the growth of (200) CrN preferentially orientation with the appearance of AlN phase. As the deposition temperature increases, the contact angle of the CrAlN surface films changes to a higher hydropholicity and the hardness of the coatings gradually increases to reach a maximum value of 28.1 GPa. The main wear mechanisms of CrAlN coating deposited at 300 ◦C against Al6061 ball are a combination of abrasive and adhesive features. This coating also has the lowest friction coefficient (0.63) and wear rate (1.72 × 10�� 3 mm3/N.m). Indeed, the preferred deposition temperature of about 300◦C could effectively adjust the microstructure and improve the mechanical and tribological properties of CrAlN coatings, thereby indicating its potential as an effective coating material for the protection of X38CrMoV5 steel in industrial fields
Prevailing effect of residual stresses and defects on the fatigue strength of net-shape parts produced with Laser Powder Bed Fusion (L-PBF) 316L stainless steel
No full textFinancement région Pays de la LoireLaser Powder Bed Fusion (L-PBF) additive manufacturing enables the production of complex-shaped parts with high mechanical resistance. Such components exhibit a multi-scale microstructure, internal, sub-surface, and surface defects, a rough surface finish and a residual stress gradient in the as-built net-shape condition. All of these factors can alter the fatigue behaviour. This study aims to improve the understanding of the combined effect of various surface parameters on the fatigue behaviour of L-PBF 316L stainless steel by conducting an extensive experimental campaign. Uni-axial fatigue tests were carried out on six batches having an as-built or heat-treated microstructure and a net-shape, pre-corroded net-shape, single-defect net-shape or polished surface condition. Results were compared with data from the literature: as-built polished, pre-corroded polished, or single-defect polished specimens. Studied defects were process-induced (e.g. lack of fusion, spatter, gas pore) and artificial (i.e. corrosion pit, electric discharge machined defect). Their sizes ranged from 10 to 700 μm. The residual stresses gradients were characterized by X-ray diffraction. A Kitagawa–Takahashi diagram was used to illustrate the effects of the various parameters on the fatigue behaviour. Residual stresses and defects were the most influential factors on the fatigue strength of net-shape specimens over surface condition and sub-surface microstructure
A methodology to bridge urban shade guidelines with climate metrics
Full text linkUrban overheating poses significant challenges to public comfort and health, particularly in pedestrian areas. While urban climate studies offer detailed maps of thermal discomfort and heat stress, urban planning often relies on simplified guidelines, creating a gap between research and practice. This study introduces a methodology to bridge this gap by developing a spatially aggregated dissatisfaction indicator, PPD*^, based on the Universal Thermal Climate Index (UTCI) and incorporating a minimum spatial requirement for shade derived from existing cities' shading policies. The novel indicator separately accounts for thermal discomfort in both shaded and sunlit pedestrian areas. A simulated case study in a neighborhood in La Rochelle, France, evaluates six tree planting scenarios, with canopy cover ranging from 0% to 80%. Results indicate that a 20% canopy cover is a practical threshold for mitigating discomfort in moderate and warm climates. This methodology can also be extended to assess additional cooling strategies, such as evaporative systems, and provides valuable insights for optimizing cost-effective and sustainable urban adaptation measures. © 202
Redefining physiological whole-body alignment according to pelvic incidence: normative values and prediction models
Full text linkBackground context
Spinopelvic alignment assessment needs to account for pelvic incidence (PI).
Purpose
This study aimed at providing normative values for commonly used parameters in whole-body alignment analysis based on PI.
Design
Multicentric prospective study.
Patient sample
This study included healthy volunteers with full-body biplanar radiograph in free-standing position.
Outcome measures
All radiographic data were collected from 3D reconstructions: Sagittal vertical axis (SVA), T1 pelvic angle (TPA), spino-sacral angle (SSA), sagittal odontoid-hip axis angle (ODHA), pelvic parameters, sacro-femoral angle (SFA), knee flexion angle (KFA), ankle flexion angle (AA), Pelvic shift (PSh), lumbar lordosis (LL), thoracic kyphosis (TK) and cervical lordosis (CL).
Methods
Population was divided into five groups according to PI. Normative values were described for each group. Linear regressions including age and PI provided prediction formulas for PT, TPA, SSA and SFA.
Results
642 subjects were included. Mean age was 37.7 ± 16.3 years (range: 18–90). Mean PI in the cohort was 49.3 ± 9.5°. LL, PT, SFA, SSA and TPA correlated with PI and age. ODHA, TK, CL and the other lower limb parameters were not associated with PI. All normative values across PI groups are provided for segmental, regional and global alignment parameters. Prediction formulas were: PT=-12.7 + 0.38*PI + 0.14*Age, TPA=-16.9 + 0.34*PI + 0.15*Age, SSA = 109.8 + 0.58*PI-0.19*Age, and SFA = 173 + 0.39*PI + 0.11*Age.
Conclusions
SSA, PT, TPA and SFA must be assessed according to patient’s PI. This study provides normative values for each PI group, and predictive formulas taking age and PI into account. PI cannot be used to define thoracic and cervical curvatures
Optimizing social interactions in VR
Full text linkTechnological developments have made Virtual Reality (VR) technologies accessible, and have democratized its use for industrial, cultural or entertainment purposes. VR can be seen as a unique medium: while being immersed together in a virtual environment (VE), people can feel the presence of each other as if they were in the same physical space even if they are far apart in reality. This phenomenon is known as co-presence. The VR medium introduces specific factors influencing on the dynamics of the social interaction. This PhD project aims at understanding that dynamics through the study of co-presence