Publications

In today’s world, composites material composed of two or more components have a significant role to play and are widely used in various applications due to their strength and stiffness [1]. Over the next few years, the EU composite market is expected to grow steadily at a rate of 7.5% [2], leading to an increase in the production and waste of composites. However, composites are complex materials due to their heterogeneous composition, making end-of-life treatment challenging [3]. Moreover, new composite production can also have a significant environmental impact as it requires using natural resources like oil. Recycling composites is, therefore, essential to reduce negative environmental impacts, optimize natural resource use, and reduce greenhouse gas emissions [4]. Though grinding and pyrolysis are common recycling methods, they can cause structural damage to the reinforcing fibers and reduce the quality of the recovered materials [5], necessitating new alternative recycling technologies. This paper aims to present the dismantling of composite materials for recycling purposes using the laser shock process, which is efficient, selective, and novel. We will optimize the process through experiments and numerical simulations, considering adhesion/material properties and process parameters. Additionally, we will study the impact of accelerated aging on the materials to simulate their end-of-life, with online control through sensors. Finally, we aim to preserve the properties of the separated materials and investigate the possibility of reassembling them for recycling purposes, ensuring the integrity of dismantled parts through non-destructive testing methods. [1] D.K.Rajak, D.D.agar, P.L.Menezes, E.Linul, Fiber-reinforced polymer composites: Manufacturing, properties, and applications, MDPI (2019). doi:doi.org/10.3390/polym11101667. [2] Avk market report (2021). [3] G. A. L. J. Rybicka, A. Tiwari, Technology readiness level assessment of composites recycling technologies, Journal of Cleaner Production 112 (2016) 1001–1012. doi:10.1016/j.jclepro.2015.08.104. [4] Y. Yang, R. Boom, B. Irion, D.-J. v Heerdenb, P. Kuiper, H. d. Wit, Recycling of composite materials, Chemical Engineering and Processing - Process Intensification (2011). arXiv:https://doi.org/10.1007/11837-001-0172-y, doi:doi.org/10.1016/j.cep.2011.09.007. URL https://doi.org/10.1007/s11837-001-0172-y [5] D. S. Cousins, Y. Suzuki, R. E. Murray, J. R. Samaniuk, A. P. Stebner, Recycling glass fiber thermoplastic composites from wind turbine blades, Journal of Cleaner Production (2019) 1252–1263doi:doi.org/10.1016/j.jclepro.2018.10.286.

Purpose A minimally invasive bipolar spinal fixation was recently developed to correct the deformity in pediatric neuromuscular scoliosis and has recently been adapted for adult scoliosis. Although the clinical results are promising, mechanical complications are still not negligible. In this work, alternative configurations of bipolar constructs were compared through numerical simulation, in order to evaluate stress distribution along the implant according to each configuration. Methods The configurations included doubling the rods, adding lumbar screws to strengthen the distal anchorage, and combining two different materials (titanium and chromium-cobalt alloy). This resulted in seven different configurations, which were implemented in a subject-specific and experimentally validated finite element model, based on the geometry of an asymptomatic subject. Von Mises stresses were compared between configurations. Results The results confirm that doubling the rods reduced mid-rod stresses, as expected, but also shifted some of the load from the distal anchorage to the rods, which is a common site of implant failure. The addition of pedicle screws also reduced the stress in the distal anchorage. The configuration showing the best compromise between stress reduction and the mini-invasive character of surgery included a doubling of both rods in titanium. Conclusions These results should be confirmed by clinical results, but they already provide clear guidelines for the surgeon.

Introduction : Understanding the normal anatomy of thoracic kyphosis (TK) in healthy subjects is essential for evaluating sagittal malalignment and planning the surgery accordingly. The aim of this study was to identify the proportion of thoracic kyphosis originating from disc versus vertebral body shape and to describe its variation according to age and thoracic kyphosis magnitude. Methods : This study was a retrospective review of a prospective multicenter database of healthy volunteers aged 18 years or older. Vertebral body and disc sagittal Cobb angles were measured at each level and summed within each of the 3 TK regions (Upper, Middle and Lower TK). Relative contributions of discs and vertebral bodies to Upper, Middle, Lower, and total TK were assessed in the whole cohort, and according to age and TK groups, after stratification. Finally, a multivariate analysis including age and TK magnitude was conducted. Results : Among these 645 subjects, the mean age was 37.6 ± 16.3 years with 51% of females. Intervertebral discs were kyphotic in Upper and Middle TK with respective discs contribution to total TK of 4.2% and 9.6%, for a total of 13.8% of total kyphosis. Lower TK discs were lordotic, with a participation of -13.2% of total TK, leading to an overall discs contribution to TK of 0.6%. Vertebral bodies were all kyphotic with a contribution of 99.4% of total kyphosis. Vertebral bodies kyphosis increased across age groups for Middle TK (p = 0.004), Lower TK (p < 0.001), and Total TK (p < 0.001). Discs contributions to total TK increased significantly with increasing TK (-13.8% for Low TK, -1.5% for Average-Low TK, 5.7% for Average-High TK and 9.1% for High TK), (p < 0.001). Finally, discs contribution was significantly greater in males than in females, with respective values of 2.6% and -1.8% (p = 0.01). Conclusion : This study highlights the predominant role of vertebral bodies contribution to thoracic kyphosis, 99.4% on average. The contribution of disc to thoracic kyphosis (values ranging from -13.8% to 9.1%) increases significantly with increasing thoracic kyphosis magnitude. The association of age with thoracic kyphosis was greater for vertebral bodies than discs, particularly in Middle and Lower TK.

The 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.

Despite decades of refinement, technology acceptance models such as the Technology Acceptance Model (TAM; Davis, 1985, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) remain the dominant frameworks for evaluating digital technologies. Their resilience reflects robustness and parsimony. Yet Artificial Intelli-gence (AI) changes the game. Unlike earlier systems, AI learns, adapts and acts, increasingly participating in the decisions, challenging the very assumptions on which TAM/UTAUT rest. As Venkatesh himself admitted, the acceptance of AI tools remains “a question mark”, raising doubts on the adequacy of established models (Venkatesh, 2022). Drawing on a semi-systematic literature review (12,048 publications from 1985–2025, including 155 focused on AI ac-ceptance), we show that while TAM/UTAUT still account for nearly 70% of studies, the field has entered a phase of conceptual displacement. Three converging dynamics stand out: an affec-tive and experiential turn, a vulnerability-centered perspective and a socio-technical orientation. Together, they crystallize into three new research streams: trust-centered, adoption-oriented and ethics-centered, that shift the field away from individual-utilitarian framings toward relational, organizational and governance logics. The challenge ahead is clear: to decide whether constructs such as trust, affect, privacy, ethics and anthropomorphism are merely contextual moderators or the building blocks of a new paradigm. The age of AI calls for more than incremental refine-ments, it demands a shared theoretical framework capable of steering organizations and societies through both the promises and risks of intelligent systems.

Reliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through force–displacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control.

Cet article propose une exploration approfondie de la réalité virtuelle (RV) en tant que technologie émergente dans l’enseignement du génie mécanique, en s’appuyant sur sa mise en œuvre sur le campus des Arts et Métiers de Metz. Il met en lumière une série de projets dirigés par des étudiants, qui exploitent la RV pour concevoir des environnements d’apprentissage immersifs. Des études expérimentales menées auprès d’étudiants de premier cycle indiquent que la RV améliore la compréhension des systèmes mécaniques complexes et favorise l’apprentissage collaboratif. L’article présente les principaux avantages de cette technologie, tels qu’un engagement accru des étudiants et une meilleure visualisation, tout en abordant les défis, notamment le mal des transports, les coûts élevés des équipements et la nécessité de former les enseignants. Les perspectives d’avenir sont également examinées, notamment l’extension des applications de la RV à d’autres disciplines académiques, la création d’expériences d’apprentissage interdisciplinaires, et l’intégration de la RV dans les dispositifs pédagogiques existants. L’étude souligne le potentiel transformateur de la RV dans l’enseignement supérieur et sa capacité à préparer les étudiants aux environnements numériques de demain.

Wire Arc Additive Manufacturing (WAAM) is an efficient technology for producing metal parts. It offers high deposition rates, low material waste and reduced machining costs. However, the process's complex multi-physics nature presents several challenges, particularly with regard to residual stresses, deposition accuracy, and internal defects. Internal defects that occur during the WAAM process are difficult to detect in real time or immediately after fabrication without the use of non destructive testing methods.This study focuses on enhancing an experimental protocol that employs a monitoring method analysing the geometry of each deposited layer using a laser sensor. This approach, combined with segmentation techniques, aims to identify and locate internal defects. Specifically, the proposed method relies on segmenting individual layers and comparing normalised values to detect and localise defects. This paper presents the developed methodology and an initial validation of its effectiveness.

Dans un contexte industriel en mutation, les entreprises manufacturières doivent allier flexibilité et performance. Depuis 2017, une recherche appliquée en partenariat avec un équipementier automobile a permis de co-concevoir des produits et des processus d’assemblage pour développer des systèmes de production modulaires et reconfigurables. Une nouvelle approche de modélisation stabilise les architectures produits, facilitant l’identification de familles de produits et l’optimisation des systèmes d’assemblage multiproduits. L’importance du positionnement et du guidage dans l’assemblage a été mise en avant, ainsi qu’un outil d’optimisation pour la sélection des ressources et l’affectation des tâches. Des solutions concrètes, telles que des lignes de production modulaires et des outils adaptables, ont été testées avec succès, réduisant les temps de reconfiguration et améliorant l’efficacité. Le transfert de connaissances a renforcé l’expertise régionale, favorisant l’innovation et la compétitivité dans l’usine du futur.

In this paper, we compare two parametric model order reduction methods, the multi-moment matching method and the interpolation of projection subspaces method for the magneto-quasistatic (MQS) and electro-quasistatic (EQS) problems derived from Maxwell’s equations and discretized with the Finite Element (FE) method. The two problems considered are both governed by the differential–algebraic equations. The material characteristic parameters as well as the geometry parameters have been considered. The applications are two realistic test cases: an EQS model of a transformer bushing under insulation defect uncertainty and a MQS model of a planar inductor with geometric and material variations. The result shows that both methods approximate well global quantities, such as the current or the voltage, as well as the local quantities like field distributions. The multi-moment matching method remains always faster in the online stage, since the reduced basis is not parameter dependent, requiring no reduced basis calculation. The multi-moment matching method requires an affine decomposition of the FE model, which is not easy to obtain when considering geometry parameters. A hybrid method is proposed and tested leading to more accurate results than the interpolation of projection subspaces method but much easier to implement than the multi-moment matching method.