Collocation-based kriging with applications to the prediction of perfect flows

National audienc

Corrosion behaviour of Alloy 600 containing oxide inclusions exposed to primary water of pressurised water reactors

International audienceIntergranular stress corrosion cracking (IGSCC) was observed in an Alloy 600 bottom-mounted instrumentation (BMI) nozzle of a French pressurised water reactor (PWR). Destructive examinations of the component revealed the presence of oxide inclusions extending along the crack, which were identified as likely to promote IGSCC initiation. In this context, the corrosion behaviour of Alloy 600 laboratory heats containing aluminium and/or magnesium oxide inclusions was investigated in primary water at 325°C and 360°C for oxidation times ranging from 10 h to 7000 h. Complete dissolution and/or detachment of the oxide inclusions was observed after a few hundred hours of exposure to primary water, and the presence of magnesium borate was locally detected. Furthermore, blister-like features consisting of Ni2FeBO5 filaments embedded in a double nickel chromite spinel layer were characterised on the surface of Alloy 600 in the vicinity of some alumina inclusions while, a micron-thick oxide layer was formed at the former interface with the pre-existing inclusions

Aggregated model for anomaly detection: a statistical learning approach

International audienceThe core of this research is a novel mathematical model for anomaly detection indiscrete manufacturing processes. Designed to identify significant deviations in production lead timewithin a production line, the model is constructed using statistical learning techniques and aggregatesmultiple individual classification models. It relies on the principles of information theory, particularlyShannon’s Entropy, to quantify uncertainty and enhance the precision of anomaly identification.Consequently, the model demonstrates high performance in distinguishing outliers from anomalies,making it applicable to real-world production data. The model is extensible and can be integrated intoproduction line control systems for real-time anomaly detection, aligning with Industry 4.0 principles,including smart manufacturing and data-driven decision-making.Le cœur de cette recherche est un modèle mathématique novateur pour la détection desanomalies dans les processus de fabrication discrète. Conçu pour identifier des écarts significatifs dansles délais de production au sein d'une ligne de production, le modèle est construit en utilisant destechniques d'apprentissage statistique et agrège plusieurs modèles individuels de classification. Ils'appuie sur les principes de la théorie de l'information, notamment l'Entropie de Shannon, pourquantifier l'incertitude et améliorer la précision de l'identification des anomalies. En conséquence, lemodèle démontre des performances élevées dans la distinction entre les valeurs aberrantes et lesanomalies, ce qui le rend applicable aux données de production réelles. Le modèle est extensible et peutêtre intégré dans des systèmes de pilotage des lignes de production pour une détection des anomalies entemps réel, s'inscrivant dans les principes de l'Industrie 4.0, notamment la fabrication intelligente et laprise de décision pilotée par les donnée

Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments

International audienceThis paper presents a multi-agent infrastructure that couples physical and digital robotic environments, enhanced by a knowledge graph. The infrastructure aims to simplify the design, modification, and evaluation of robotic systems. We propose to use a knowledge graph to represent environmental data, robot states, and interactions. This enables supervision of both physical and simulated robots on existing and heterogeneous platforms. Deployed in the Hypermedea multi-agent programming environment, agents use the knowledge graph to facilitate seamless interaction and coordination across different platforms. The feasibility of the infrastructure is demonstrated through a coordination scenario between a simulated robotic arm and a physical mobile robot in Industry 4.0

Extending nanoindentation testing toward extreme strain rates and temperatures for probing materials evolution at the nanoscale

International audienceFor the past 30 years, nanoindentation has provided critical insights into the microstructure– strength relationship for a wide range of materials. However, it has traditionally been limited to quasistatic testing at room temperature, which has hindered a holistic understandingof microstructurally induced deformation mechanisms and their dynamic evolution as a function of the temperature and strain rate. Over the past decade, the operational scope of nanoindentation has expanded dramatically. Temperatures up to 1100°C and strain rates as high as 10 +4 s −1 and as low as 10 −8 s −1 have become accessible. In addition, advanced techniques allow tracking microstructural evolution and corresponding changes in mechanical behavior during deformation under extreme conditions. These advancements have transformed nanoindentation into a versatile tool for comprehensive materials characterization, enablinghigh-throughput investigations under multimodal conditions

Enhanced strain-hardening in newly designed Co-free austenitic high entropy alloys with an optimised nitrogen solubility

International audienceThe effect of nitrogen on the microstructure and mechanical behavior of several Co-free high entropy alloys (HEAs) from the CrFeMnNi family was studied. Alloy design approach, based on thermodynamic computations, was implemented to obtain alloys with a high chromium content, a high nitrogen solubility as well as good austenite stability. An optimisation was made to meet selected criteria which led to three compositions with different nickel contents (Cr20Fe40Mn15Ni25, Cr20Fe44Mn15Ni21, and Cr20Fe47Mn15Ni18) optimised for, respectively, 0.4, 0.5 and 0.6 wt% N. Such optimised alloys were elaborated and compared with another Co-free HEA (Cr14Fe46Mn17Ni23) doped with 0.11–0.29 wt% N. It was shown that up to 0.56 wt% N could be dissolved in the matrixes in agreement with their computed high nitrogen solubility. The differences in chemical composition between the four studied matrixes did not lead to any change in behavior in presence of nitrogen: the lattice parameter expansion and mechanical resistances (yield strength and tensile strength) evolve linearly with nitrogen addition, up to at least 0.56 wt%. Especially, a significant effect of nitrogen content on strain-hardening was observed and was attributed to the formation of nanotwins in nitrogen-rich alloys

A Unified View on Regulation Management in Multi-Agent Systems

International audienceRegulating multi-agent system (MAS) to achieve a balance between the autonomy of agents and the control of the system is still a challenge. Regulation management in MAS has been conceptualized from various perspectives in the literature, whose intersections open up a wide range of design options. We propose a unified view on regulation management in MAS that identifies the range of design options with respect to three perspectives: the regulation capabilities, the multi-agent oriented programming dimensions, and the architectural style. We use our unified view to review and classify existing MAS frameworks in the literature, highlighting the dominant and underexplored views on regulation management in MAS

Bayesian optimization with derivatives acceleration

Guillaume Perrin, first author. Rodolphe Le Riche, second author, invited speaker of the workshop.National audienceBayesian optimization algorithms form an important class of methods to minimize functions that are costly to evaluate, which is a very common situation. These algorithms iteratively infer Gaussian processes from past observations of the function and decide where new observations should be made through the maximization of an acquisition criterion. Often, in particular in engineering practice, the objective function is defined on a compact set such as in a hyper-rectangle of a d-dimensional real space, and the bounds are chosen wide enough so that the optimum is inside the search domain. In this situation, this work provides a way to integrate in the acquisition criterion the a priori information that these functions, once modeled as GP trajectories, should be evaluated at their minima, and not at any point as usual acquisition criteria do. We propose an adaptation of the widely used Expected Improvement acquisition criterion that accounts only for GP trajectories where the first order partial derivatives are zero and the Hessian matrix is positive definite. The new acquisition criterion keeps an analytical, computationally efficient, expression. This new acquisition criterion is found to improve Bayesian optimization on a test bed of functions made of Gaussian process trajectories in dimensions 2, 3 and 5. The addition of first and second order derivative information is particularly useful for multimodal functions

A New Digital Twins Technique for Minimizing Setup Times in Reconfigurable Stations of a Mixed Model Flow Lines

International audienc

Anomaly Detection in a Production Line: Statistical Learning Approach and Industrial Application

International audienc