Laboratoire Génie de Production

Breakthroughs in materials science are the driving force behind many of today's industrial advancements in our fast-changing high-tech world. Composite materials have proven valuable in numerous sectors, including automotive, aerospace, aeronautics, naval, and sports, due to their exceptional mechanical properties and lightweight nature. However, environmental concerns have led to a decrease in the use of fossil fuel-derived materials. Additionally, efforts to reduce greenhouse gas emissions and improve fuel efficiency require lightweight materials with a lower carbon footprint, highlighting the importance of natural fiber composites. Natural fiber composites are made from renewable resources, comprising reinforcements made of natural fibers such as jute, flax, ramie, hemp, cotton, sisal, and kenaf, and a matrix, preferably derived from biomass, which may or may not be biodegradable. However, plant fibers have certain drawbacks when combined with polymers. Due to the presence of hydroxyl groups in lignocellulose, plant fibers are hydrophilic, making them incompatible with hydrophobic thermoplastics and prone to moisture damage. These limitations pose challenges for using plant fibers as polymer reinforcement. To improve adhesion between fibers and the polymer matrix and reduce moisture absorption, surface modifications are typically required. Various methods, such as alkaline, silane, or other chemical treatments, have been developed to enhance fiber-matrix compatibility and improve composite quality. Although natural fiber composites are still in development and their applications are limited, they hold great promise as a sustainable alternative to conventional materials.

A comparative study of the crystallinity of Polyetheretherketone by using density, DSC, XRD, and Raman spectroscopy techniques. In this work, the microstructure of Polyetheretherketone is first analyzed with usual techniques such as density, Differential Scanning Calorimetry, X-ray Diffraction, and secondly, compared with Raman Spectroscopy. Assessing the degree of crystallinity of PEEK is challenging because of the different interpretation of the crystallinity according to each technique. The density measurement gives the highest most trusted absolute uncertainty for the degree of crystallinity, around 4%, compared to the other techniques. The Differential Scanning Calorimetry, usually used by the polymer community, overestimates up to 18% the degree of crystallinity due to a competitive phenomenon between crystallization and melting of PEEK over the same temperature range, and a fast crystallization. When Analyzing the X-ray Diffraction data, the degree of crystallinity is underestimated up to 11% as a consequence of the broad amorphous halo. Lastly, our investigation proves that Raman microspectroscopy is appropriate to determine the local crystallinity on the sample surface and compares 18 indicators in the same study. The 1651 cm-1 band shift has the highest correlation coefficient of 0.92 with the degree of crystallinity determined by density. This work attempts to correlate the results of degree of crystallinity of PEEK obtained by these four techniques in order to establish the best evaluation of this fundamental property for numerous applications.

The most widely used technique for generating whole-body motions on a humanoid robot accounting for various tasks and constraints is inverse kinematics. Based on the task-function approach, this class of methods enables the coordination of robot movements to execute several tasks in parallel and account for the sensor feedback in real time, thanks to the low computation cost. To some extent, it also enables us to deal with some of the robot constraints (e.g., joint limits or visibility) and manage the quasi-static balance of the robot. In order to fully use the whole range of possible motions, this paper proposes extending the task-function approach to handle the full dynamics of the robot multibody along with any constraint written as equality or inequality of the state and control variables. The definition of multiple objectives is made possible by ordering them inside a strict hierarchy. Several models of contact with the environment can be implemented in the framework. We propose a reduced formulation of the multiple rigid planar contact that keeps a low computation cost. The efficiency of this approach is illustrated by presenting several multicontact dynamic motions in simulation and on the real HRP-2 robot.

Certain planning systems that deal with quantitative time constraints have used an underlying Simple Temporal Problem solver to ensure temporal consistency of plans. However, many applications involve processes of uncertain duration whose timing cannot be controlled by the execution agent. These cases require more complex notions of temporal feasibility. In previous work, various "controllability" properties such as Weak, Strong, and Dynamic Controllability have been defined. The most interesting and useful Controllability property, the Dynamic one, has ironically proved to be the most difficult to analyze. In this paper, we resolve the complexity issue for Dynamic Controllability. Unexpectedly, the problem turns out to be tractable. We also show how to efficiently execute networks whose status has been verified.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations.

This work presents a new multilayered laminated composite structure model to predict the mechanical behaviour of multilayered laminated composite structures. This new multilayered structure model describes the shear stress distribution model through the thickness respecting free boundary conditions on the top and bottom surfaces by an exponential function. This model has the same order of complexity as Touratier's model ‘Sine’, so a shear correction factor is not required like in the first-order shear deformation theory. This model is more precise than all other existing refined theories. This theory is based on the kinematic approach in which the shearing is represented by an exponential function. The virtual power principal is used to deduce the boundary value problem. To verify the precision of the present model, several significant problems on bending, vibration, and buckling of laminated and sandwich structures have been studied. The results by the present model are compared with the exact three-dimensional elasticity theory and with several other well-known theories. The proposed model is found to be more precise for analysing multilayered structures.

International audience

Machining is a material removal process that alters the dynamic properties during machining operations. The peripheral milling of a thin-walled structure generates vibration of the workpiece and this influences the quality of the machined surface. A reduction of tool life and spindle life can also be experienced when machining is subjected to vibration. In this paper, the linearized stability lobes theory allows us to determine critical and optimal cutting conditions for which vibration is not apparent in the milling of thin-walled workpieces. The evolution of the mechanical parameters of the cutting tool, machine tool and workpiece during the milling operation are not taken into account. The critical and optimal cutting conditions depend on dynamic properties of the workpiece. It is illustrated how the stability lobes theory is used to evaluate the variation of the dynamic properties of the thin-walled workpiece. We use both modal measurement and finite element method to establish a 3D representation of stability lobes. The 3D representation allows us to identify spindle speed values at which the variation of spindle speed is initiated to improve the surface finish of the workpiece.

For generations, the process of cost estimation has been manual, time-consuming and error-prone. Emerging Building Information Modelling (BIM) can exploit standard measurement methods to automate cost estimation process and improve inaccuracies. Structuring standard measurement methods in an ontologically and machine readable format for a BIM software can greatly facilitate the process of improving inaccuracies in cost estimation. This study explores the development of an ontology based on New Rules of Measurement (NRM) for cost estimation during the tendering stages. The methodology adopted is methontology, one of the most widely used ontology engineering methodologies. To ensure the ontology is fit for purpose, cost estimation experts are employed to check the semantics, descriptive logic-based reasoners are used to syntactically check the ontology and a leading 4D BIM modelling software is used on a case study building to test/validate the proposed ontology.

This paper presents a condition monitoring approach for point machine prognostics to increase the reliability, availability, and safety in railway transportation industry. The proposed approach is composed of three steps: 1) health indicator (HI) construction by data fusion, 2) health state assessment, and 3) failure prognostics. In Step 1, the time-domain features are extracted and evaluated by hybrid and consistency feature evaluation metrics to select the best class of prognostics features. Then, the selected feature class is combined with the adaptive feature fusion algorithm to build a generic point machine HI. In Step 2, health state division is accomplished by time-series segmentation algorithm using the fused HI. Then, fault detection is performed by using a support vector machine classifier. Once the faulty state has been classified (i.e., incipient/starting fault), the single spectral analysis recurrent forecasting is triggered to estimate the component remaining useful life. The proposed methodology is validated on in-field point machine sliding-chair degradation data. The results show that the approach can be effectively used in railway point machine monitoring.

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

ABSTRACT We report on the main results of a collaborative work devoted to the study of the uncertainties associated with Digital image correlation techniques (DIC). More specifically, the dependence of displacement measurement uncertainties with both image characteristics and DIC parameters is emphasised. A previous work [Bornert et al . (2009) Assessment of digital image correlation measurement errors: methodology and results. Exp. Mech. 49, 353–370] dedicated to situations with spatially fluctuating displacement fields demonstrated the existence of an ‘ultimate error’ regime, insensitive to the mismatch between the shape function and the real displacement field. The present work is focused on this ultimate error. To ensure that there is no mismatch error, synthetic images of in‐plane rigid body translation have been analysed. Several DIC softwares developed by or in use in the French community have been used to explore the effects of a large number of settings. The discrepancies between DIC evaluated displacements and prescribed ones have been statistically analysed in terms of random errors and systematic bias, in correlation with the fractional part τ of the displacement component expressed in pixels. Main results are as follows: (i) bias amplitude is almost always insensitive to subset size, (ii) standard deviation of random error increases with noise level and decreases with subset size and (iii) DIC formulations can be split up into two main families regarding bias sensitivity to noise. For the first one, bias amplitude increases with noise while it remains nearly constant for the second one. In addition, for the first family, a strong dependence of random error with τ is observed for noisy images.

Temperature prediction of a battery plays a significant role in terms of energy efficiency and safety of electric vehicles, as well as several kinds of electric and electronic devices. In this regard, it is crucial to identify an adequate model to study the thermal behavior of a battery. This article reports a comparative study on thermal modeling approaches by using a LiCoO2 26650 lithium-ion battery, and provides a methodology to characterize electrothermal phenomena. Three approaches have been implemented numerically—a thermal lumped model, a 3D computational fluid dynamics model, and an electrochemical model based on Newman, Tiedemann, Gu and Kim formulation. The last two methods were solved using ANSYS Fluent software. Simulations were validated with experimental measurements of the cell surface temperature at constant current discharge and under a highway driving cycle. Results show that the three models are consistent with actual temperature measurements. The electrochemical method has the lower error at 0.5C. Nevertheless, this model provides the higher error ( 1.3∘C) at 1.5C, where the maximum temperature increase of the cell was 18.1∘C. Under the driving cycle, all the models are in the same order of error. Lumped model is suitable to simulate a wide range of battery operating conditions. Furthermore, this work was expanded to study heat generation, voltage and heat transfer coefficient under natural convection.

The corrosion behavior of a Friction Stir Welding joint in 2050-T3 Al-Cu-Li alloy was studied in 1 M NaCl solution and the influence of T8 post-welding heat treatment on its corrosion susceptibility was analyzed. After exposure to 1 M NaCl solution, the heat affected zone (HAZ) of the weld without post-welding heat treatment was found to be the most extensively corroded zone with extended intergranular corrosion damage while, following T8 post-welding heat treatment, no intergranular corrosion was observed in the HAZ and the global corrosion behavior of the weld was significantly improved. The corrosion damage observed on the welded joints after immersion in 1 M NaCl solution was compared to that obtained after 750 h Mastmaasis Wet Bottom tests. The same corrosion damage was observed. Various stationary electrochemical tests were carried out on the global welded joint and/or each of the metallurgical zones of the welded joint to understand the corrosion damage observed. TEM observations helped in bringing meaningful elements to analyze the intrinsic electrochemical behavior of the different zones of the weld related to their microstructure. However, galvanic coupling tests showed that galvanic coupling effects between the different zones of the weld were at least partially responsible for its corrosion behavior.

4D printing is defined as the additive manufacturing process of smart (stimuli‐responsive) materials. Shape memory materials are sensitive to specific stimuli such as heat, electricity, magnetic fields, etc., which can change their form or properties. This characteristic gives the material a dynamic behavior over time (the fourth dimension). The application of the 4D printing technique is currently being explored in various fields, including soft robotics, electrical devices, deployable structures, medical implants, and medicine delivery systems. This article first examines the fundamentals of 3D printing techniques, their advantages, and limitations. Then, the shape memory materials are categorized and reviewed according to their type (shape memory polymers, shape memory composites, polymer blends, etc.) and stimulus responsiveness. Finally, different properties of shape memory materials like shape memory effect, thermomechanical properties, and their compatibility with different types of additive manufacturing processes are discussed.

Past maintenance logs may encapsulate meaningful data for predicting the duration of machine breakdowns, the potential causes of a problem, or the necessity to stop production to perform repair activities. These insights may be accessed using machine learning (ML). However, maintenance logs tend to have imbalanced distributions and rely on noisy unstructured text data provided by operators. Additionally, the limited interpretability of ML models results in human reluctance when accepting model predictions. Hence, this study explored the use of two recent deep learning models (CamemBERT and FlauBERT) for natural language processing (NLP) to harness unstructured data from maintenance logs. The class imbalance effect was mitigated using data-level and algorithm-level approaches. To improve interpretability, a technique called LIME was employed to interpret single predictions and to propose a method for insight extraction from several maintenance reports. Results suggest three key points: CamemBERT and FlauBERT can achieve excellent results with minimum text pre-processing and hyperparameter tuning. Second, random oversampling (ROS) generally mitigates the effect of class imbalance. However, ROS was observed to be unnecessary when performing pertinent data pre-processing. Finally, at the maintenance level, the proposed insight extraction method can provide valuable information from a set of poorly structured maintenance reports.

BACKGROUND: In view of the large consumption of herbal medicine in Africa countries, it is likely that many adverse drugs reactions go unrecorded with either patients failing to present to health services, or no pharmacovigilance analysis being made, or the analysis not being reported centrally. This problem is of interest especially for those who are working in the general area of adverse drug reactions or stakeholders in the domain of herbal medicine for considering safety issues. METHODS: We are particularly interested in the way that the use of very well-known and highly valued plants is linked to the observation of adverse drug reactions in African countries. We investigated, through a literature review and using the Internet (with a semantic search strategy), some well-known or popular medicinal plants used in African herbal medicine (AHM). Other information on the properties related to use, and characteristics of medicinal plants was complemented by some interviews with stakeholders. RESULTS: Although substantial progress has been made in elucidating the mechanisms of action of many drugs, the pharmacological actions of many medicinal plants are generally not well understood. The results of a literature review suggest that the reported adverse drug reactions of herbal remedies are often due to a lack of understanding of their preparation and appropriate use. The results of stakeholders' interviews suggest that there is a growing need to provide patients with correct information about the herbal medicines they consume. CONCLUSION: An important aspect of herbal medicine is the correct, timely, and integrated communication of emerging data on risk as an essential part of pharmacovigilance, which could actually improve the health and safety of patients. This calls for improved collaboration between traditional practitioners and modern healthcare professionals, researchers, and drug regulatory authorities. In addition, there is a need for an adverse drug reaction reporting system to facilitate the collection, monitoring, and evaluation of adverse drug events.

Abstract An arbitrary Lagrangian‐Eulerian model is described for the prediction of metal flow in a steady‐state forming process. The formulation is shown to be capable of dealing with free surface and boundary friction; the coupling with thermal effects is also considered. The numerical techniques are based on the finite elements for spatial discretization of the momentum equation and on an explicit integration scheme for discretization relative to time. The mass and energy equations are integrated by a finite‐volume method. Details on the application to an orthogonal cutting process are given in the final Section of the paper.

An IR-femtosecond laser ablation ICPMS coupling was used to investigate the influence of the high repetition rate on elemental fractionation effects for the analysis of silicate glass SRM NIST 610. First, elemental fractionation inherent to the ICP was minimised by working on wet plasma conditions which had greater tolerance to mass loading and demonstrated a higher robustness compared to dry plasma conditions. Because of the use of a narrow laser beam producing small craters (17 µm in diameter), a special arrangement of pulses was used to perform resulting craters of 100 µm diameter. The ablation strategy developed in this work consisted in a series of concentric circle trajectories ablated at high repetition rates by moving the laser beam rapidly thanks to a scanning beam device. Two scanner speeds (0.25 mm s−1 and 1.5 mm s−1), five laser repetition rates (from 0.1 kHz to 10 kHz) and three fluence values (5 J cm−2, 14 J cm−2, and 25 J cm−2) were investigated in detail. For this purpose, critical elemental ratios (namely 238U/232Th, 208Pb/238U, and 66Zn/65Cu) of aerosols produced by fs-LA of silicate glass were studied to evaluate the impact of the different laser parameters on elemental fractionation. No heating zones or preferential evaporation of elements were found depending on the repetition rate employed. However, particle-size-fractionation was measured during the ablation of the sample surface, and this effect was reduced by using a high repetition rate as well as a high scanner speed which allow the dilution of the large particles coming from the surface layer with finer particles coming to deeper levels. Additionally, the ablation rate induced by the selected ablation strategy had a low influence on fractionation effects due to the high robustness of the ICP plasma and, on the other hand, fractionation indices were not particularly affected by the laser repetition rate although they could be improved by the use of high fluence values. Finally, it could be stressed that no differences on the structure of the aerosol particles collected on membrane filters were found depending on the ablation parameters.

Abstract A new eight‐node quadrilateral shear‐bending Reissner–Mindlin plate finite element for the very thin and thick plates without locking and spurious zero‐energy modes is presented. The element has very good convergence characteristics both for thin and thick plates, is hardly insensitive to mesh distortions, and passes the patch tests. The formulation of the element is derived from a displacement variational principle and some general criteria to compute inconsistent transverse shear strains. These criteria have been applied with success to four‐ and eight‐node quadrilateral plate finite elements and could be applied to construct triangular elements. The eight‐node quadrilateral shear‐bending plate finite element proposed has been found to be very efficient.