Integration of tolerances in the mechanical product process: Assembly with defects modelling.

In the digital mock-up, parts and assemblies are used in ideal configurations. In fact, tolerances are represented as annotation on the CAD model and tolerances stack-up is not considered during the optimization of mechanical system assemblability and F.E Analysis. Then, the improving of the numerical model requires consideration of tolerances in the CAD model. An approach to incorporate dimensional and geometrical tolerance in CAD model is developed. Indeed, models, that allow obtaining assemblies with defects, are realized. In a bottom-up design process (component-to-assembly), the proposed model founds components with defects allowed by tolerances. Components with defects are deduced from the combination of two model sets which are obtained by using two sub-algorithms. A first sub-algorithm incorporates dimensional tolerances in CAD model. This model founds relationships between component dimensions (driving and driven dimensions) and performs a three dimension tolerancing chain by a technique based on connected graphs. The second sub-algorithm tacks into account geometrical tolerances in CAD model. Tolerance zone is discretized by parameters deduced from deviation torsor of toleranced element. This discretization allows obtaining possible realistic configuration of the toleranced element. Then, face movements are realized to obtain components with defects. Thus, assemblies with defects can be obtained by performing various combinations between components with defects. Nevertheless, the assembly regeneration, with realistic components, requires redefining assembly mates which are initially assigned to nominal assembly. Then, a new approach to defining realistic assembly mates in the case of rigid assembly is presented

An approach to unique transfer and allocation of tolerances considering manufacturing difficulty

Tolerancing is a key step in the product life cycle and aims at improving the product quality and its assemblability as well as reducing the overall costs and time to market. Especially, the tolerance allocation and transfer are two important engineering functions involving a direct impact on compliance with functional and manufacturing requirements. However, on the one hand the traditional approaches reduce the tolerance values during the transfer of design dimension on manufacturing dimensions, and on the other hand neglect the difficulty of manufacturing dimension obtaining. Thus, this paper proposes an unique transfer approach of mechanism-dimension allowing the transposition of the functional requirement into part manufacturing dimensions. In addition, this work uses an innovative tolerance allocation method considering the difficulty of obtaining manufacturing dimensions. This difficulty is evaluated through a mathematical coefficient calculated using the Failure Mode, Effects and Criticality Analysis (FMECA) tool. The failure causes are the different sources of the manufacturing difficult. The obtained results lead to avoid tolerance reduction generated by the double dimensions transfer of traditional industrial approaches. Moreover, the manufacturing dimension tolerances, which are difficult to obtain, are widen. Therefore, the total costs, considering manufacturing cost and quality loss, decreases. The main contributions of the proposed model are shown through a case study

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Brain-computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms

Stress recognition using connected devices: experimentation feedback

International audienceThe research purpose is to study the impact’s use of connected devices on cardiovascular diseases. One of our objective is stress recognition during daily routine life. To achieve that a long term experimentation have been conducted. This longitudinal empirical research study aided by connected devices enabling psycho-physiological monitoring to study the correlation between emotional states including stress levels and prognosis of cardiovascular disease. In this paper, we will present the main participant’s feedback concerning the conducted experimentation using wearables sensors. This manuscript explains participant selection. It will not treat data conducted from this experiment or show numerical results. It is a statistical feedback concerning the experimentation protocol. We will clarify the protocol strengths and weaknesses. Our main motivation is to give an opinion to other research and to inspire them if they want to conduct a similar experimentation

An investigation on the potential of utilizing aluminum alloys in the production and storage of hydrogen gas

The interest in hydrogen is rapidly expanding because of rising greenhouse gas emissions and the depletion of fossil resources. The current work focuses on employing affordable Al alloys for hydrogen production and storage to identify the most efficient alloy that performs best in each situation. In the first part of this work, hydrogen was generated from water electrolysis. The Al alloys that are being examined as electrodes in a water electrolyzer are 1050-T0, 5052-T0, 6061-T0, 6061-T6, 7075-T0, 7075-T6, and 7075-T7. The flow rate of hydrogen produced, energy consumption, and electrolyzer efficiency were measured at a constant voltage of 9 volts to identify the Al alloy that produces a greater hydrogen flow rate at higher process efficiency. The influence of the electrode surface area and water electrolysis temperature were also studied. The second part of this study examines these Al alloys’ resistance to hydrogen embrittlement for applications involving compressed hydrogen gas storage, whether they are utilized as the primary vessel in Type 1 pressure vessels or as liners in Type 2 or Type 3 pressure vessels. Al alloys underwent electrochemical charging by hydrogen and Charpy impact testing, after which a scanning electron microscope (SEM) was used to investigate the fracture surfaces of both uncharged and H-charged specimens. The structural constituents of the studied alloys were examined using X-ray diffraction analysis and were correlated to the alloys’ performance. Sensitivity analysis revealed that the water electrolysis temperature, electrode surface area, and electrode material type ranked from the highest to lowest in terms of their influence on improving the efficiency of the hydrogen production process. The 6061-T0 Al alloy demonstrated the best performance in both hydrogen production and storage applications at a reasonable material cost

Internet des Objets, maladies cardiovasculaires et détection des émotions

Les maladies cardiovasculaires sont très courantes dans divers pays en raison du stress, du diabète, du tabagisme, de l'hypertension artérielle et de certains autres facteurs liés à la santé. Il a été rapporté que 33% des décès sont dus à des maladies cardiovasculaires en France. Ces travaux de recherche ont été menés pour réduire et contrôler de manière robuste les maladies cardiovasculaires. La vie quotidienne des gens était surveillée en permanence pour analyser la santé cardiaque. Habituellement, les états émotionnels peuvent être classés en deux classes génériques telles que stressé (anxiété, déprimé) et détendu (paisible, calme). Une analyse à long terme de plus de cinq minutes et une analyse à court terme d’une durée de trois minutes ont été effectuées pour mesurer certaines variations physiologiques et émotionnelles. Il était très complexe de discriminer les variations de fréquence cardiaque pour reconnaître l'état émotionnel exact d'une personne car les données contenaient des valeurs manquées. Les capteurs les plus appropriés et les plus novateurs ont été sélectionnés sur la base d'une vaste revue de la littérature et des conseils du médecin appelés tensiomètre Rossmax (tensiomètre), ActiGraphGT9XLink (accéléromètre pour enregistrer l'activité physique) et ceinture d'identification de fréquence cardiaque Polar H7 pour la mesure des émotions. Des jeux stressants ont été développés pour collecter des données précises sous forme de données composées de valeurs manquées et redondantes. L'identification des émotions a été étudiée avec succès en utilisant la variabilité de la fréquence cardiaque (VRC) car la fréquence cardiaque varie en raison du stress, de l'activité physique, du sommeil et de la méditation. Généralement, un VRC élevé est reconnu comme l'indicateur de la santé cardiaque. Plusieurs études de cas ont été réalisées pour résoudre ce problème de santé mondiale. L'interpolation spline a été adoptée pour l'analyse numérique en raison de son erreur quadratique minimale. Pour classer de manière robuste l'état des émotions, des classificateurs d'apprentissage automatique tels que le clustering spectral, des K-moyennes, des modèles de mélange gaussien et des algorithmes de Birch ont été appliqués à l'ensemble de données. L'analyse comparative de ces algorithmes a prouvé que le Birch était plus performant que les approches existantes pour classer les émotions pour les données mises à l'échelle et non mises à l'échelle.Cardiovascular diseases are very common in various countries due to the stress, di- abetes, smoking, high blood pressure and some other health related factors. It has been reported that thirty-three percent deaths are due to cardiovascular diseases in France. This research work has been carried out to reduce and control the cardiovascular dis- ease robustly. The daily routine life of people was monitored continuously to analyze the heart health. Usually emotional states can be categorized into two generic classes such as stressed (anxiety, depressed) and relaxed (peaceful, calm). Long term analy- sis of more than five minutes and short term analysis with three minutes’ duration was performed to measure certain physiological and emotional variations. It was very com- plex to discriminate the heart rate variations to recognize the exact emotional state of a person as the data contained missed values. Most appropriate and novel sensors were selected on the basis of extensive literature review and physician’s advice named as Rossmax Tensiometer (Blood Pressure Monitor), ActiGraphGT9XLink (Accelerome- ter for recording Physical activity) and Polar H7 heart rate identifier belt for emotions measurement. Stressful games were developed to collect the accurate data as the data comprised of missed and redundant values. The emotions identification was success- fully investigated by using heart rate variability (HRV) as heart rate varies due to stress, physical activity, sleep and meditation. Commonly High HRV is acknowledged as the healthy heart indicator. Several case studies have been carried out to resolve this global health issue. Spline interpolation was adopted for numerical analysis due to its mini- mum quadratic error. To classify the emotion state robustly machine learning classifiers such as spectral clustering, K-means, Gaussian mixture models and Birch algorithms were applied to the data set. Comparative analysis of these algorithms proved that the Birch performed better than the existing approaches in classifying the emotions for scaled and unscaled data

Evaluating the effect of tolerances on the functional requirements of assemblies

In order to improve digital mock-up, a tolerancing phase should be integrated in the geometric models. However, in CAD software, tolerances are represented by annotations, which are neglected as well as the tolerance impact. Thus, the system malfunction is generated. For these reasons, in this paper a tolerancing phase is integrated in the numerical model to form a realistic model, where worst case configurations of assemblies are determined from the tolerances assigned to the nominal model. The proposed model incorporates tolerances on CAD models in the case of planar face, cylindrical face and planar face with non quadratic loop. In addition, the model ability to respect the maximum material condition (MMC) and the requirement of datum priority order in the CAD models is shown. Finally, functional requirement of a linear guide mechanism is inspected by using the proposed model

Missing-data imputation using wearable sensors in heart rate variability

International audienceThe objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error

Reconstruction d’un modèle CAO à partir d’un maillage déformé

De nos jours, nous assistons à un développement du travail en mode projet qui se caractérise par la mobilisation de compétences multiples. La globalisation des marchés ainsi que la réduction des coûts et des délais de développement de nouveaux produits ont conduit à la mise au point d’outils de travail collaboratif assurant la structuration, le suivi, et la traçabilité des échanges. Cela a induit un accroissement considérable des besoins de communication inter-applications et de cohérence globale des systèmes supports des différents modèles du produit (CAO, calcul, FAO). De part leur forte interdépendance, les deux activités CAO et calcul seront donc amenées à s’édifier sur de nouvelles technologies émergentes dans les domaines de la modélisation des données (modèle de produit) et des processus (interopérabilité) afin de pouvoir prendre en compte la manipulation d’objets hétérogènes (géométrie, sollicitations, maillage, déformation, etc.). En effet, dans un contexte de travail collaboratif, l’intégration numérique de ces deux activités CAO et calcul, est devenue une des principales préoccupations en CFAO. L’objectif recherché est de favoriser le partage des données sans recopies ou transformations manuelles afin de fluidifier les flux d’informations entre CAO et calcul tout en garantissant la fiabilité et la traçabilité des données. L’objet de cet article est de présenter une approche d’intégration numérique et d’interopérabilité des processus de CAO et de calcul mécanique qui consiste à retrouver le modèle CAO déformé à partir d’un maillage déformé dans la démarche de retour des résultats de calcul éléments-finis vers la CAO

Unique transfer of functional requirements into manufacturing dimensions in an interactive design context

International audienc