Laboratoire de Conception Fabrication Commande

Describes the design, construction and control of an experimental bipedal robot platform for the study of walking.

High O(2) consumption, modest antioxidant defenses and a lipid-rich constitution make the brain highly vulnerable to redox imbalances. Oxidative damage in the brain causes nervous system impairment. Recently, oxidative stress has also been implicated in depression, anxiety disorders and high anxiety levels. The findings which establish a link between oxidative stress and pathological anxiety have inspired a number of other recent studies focusing on the link between oxidative status and normal anxiety and also on a possible causal relationship between cellular oxidative stress and emotional stress. This review examines the recent discoveries made on the link between oxidative status and normal anxiety levels and the putative role of oxidative stress in genesis of anxiety. We discuss the different opinions and questions that exist in the field and review the methodological approaches that are being used to determine a causal relationship between oxidative and emotional stress.

This study evaluates the levels of total polyphenolic compounds in three Malian medicinal plants and determines their antioxidant potential. Quantitative and qualitative analysis of polyphenolics contained in plants extracts were carried out by RP-C18 RP-HPLC using UV detector. The antioxidant activity was determined by three tests. They are phosphomolybdenum, DPPH (2,2-diphenyl-1 picrylhydrazyl) and ABTS [2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic)] tests. The total phenolic and the total flavonoid contents varied from 200 to 7600 mg 100 g(-1) dry weight (dw), expressed as gallic acid equivalents and from 680 to 12 300 mg 100 g(-1) dw expressed as catechin equivalents, respectively. The total anthocyanin concentrations expressed as cyanin-3-glycoside equivalent varied from 1670 to 28 388 mg 100 g(-1) dw. The antioxidant capacity was measured by determining concentration of a polyphenolic (in mg ml(-1)) required to quench the free radicals by 50% (IC(50)) and expressed as vitamin C equivalent antioxidant capacity. The IC(50) values were ranked between 2.68 and 8.80 μg ml(-1) of a solution of 50% (v/v) methanol in water. The uses of plants are rationalized on the basis of their antioxidant capacity.

International audience

A helicopter maneuvers naturally in an environment where the execution of the task can easily be affected by atmospheric turbulence, which leads to variations of its model parameters. This paper discusses the nature of the disturbances acting on the helicopter and proposes an approach to counter the effects. The disturbance consists of vertical and lateral wind gusts. A 7-degrees-of-freedom (DOF) nonlinear Lagrangian model with unknown disturbances is used. The model presents quite interesting control challenges due to nonlinearities, aerodynamic forces, under actuation, and its non-minimum phase dynamics. Two approaches of robust control are compared via simulations with a Tiny CP3 helicopter model: an approximate feedback linearization and an active disturbance rejection control using the approximate feedback linearization procedure. Several simulations show that adding an observer can compensate the effect of disturbances. The proposed controller has been tested in a real-time application to control the yaw angular displacement of a Tiny CP3 mini-helicopter mounted on an experiment platform.

Assigning nurses to appropriate departments and work shifts based on human factors can strengthen teamwork and boost the efficiency of healthcare systems. The human factors considered in this study include skill, preference, and compatibility of nurses. In this regard, a unique multi-objective mathematical model for nurse scheduling is proposed in this article, in which nurses' decision-making styles are taken into account. Three objectives, including minimization of the total cost of staffing, minimization of the sum of incompatibility among nurses' decision-making styles assigned to the same shift days, and maximization of the overall satisfaction of nurses for their assigned shifts, are addressed in this model. Three meta-heuristics, namely, multi-objective Keshtel algorithm, non-dominated sorting genetic algorithm II, and multi-objective tabu search, are developed to solve the problem. Moreover, a data envelopment analysis method is employed to rank the obtained Pareto solutions. Afterwards, a real-life case at a large hospital in Tehran, Iran, is investigated. Eventually, the applicability and effectiveness of the proposed model are assessed based on the experimental results.

In multi-stage manufacturing systems, optimisation of part quality inspection planning (PQIP) problem means to determine the optimal time, place and extent of inspection activities for assessing the significant quality characteristics of products while maximising the system efficiency. An inspection activity is capable of detecting the produced defects partially and accordingly prevents further processing of them in downstream and more importantly avoids them to reach customers. In this paper, the existing researches on the optimisation of the part quality inspection are surveyed from the viewpoint of the considered production system characteristics; the applied modelling approaches and solution methodologies. This review found that although numerous works have been already done on the PQIP, the development of multi-objective optimisation frameworks considering real production constraints under parameters uncertainty is necessary. Also, by the Industry 4.0 trend, the creation of integrated models aiming to plan the inspection, maintenance and production activities simultaneously, seems to be an important potential future research direction.

In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to be used in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs better for a particular type of dataset. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to deal with QA problems. This review provides a comprehensive analysis of the literature from various points of view as DM concepts, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

Sparsity of a classifier is a desirable condition for high-dimensional data and large sample sizes. This paper investigates the two complementary notions of sparsity for binary classification: sparsity in the number of features and sparsity in the number of examples. Several different losses and regularizers are considered: the hinge loss and ramp loss, and ℓ2, ℓ1, approximate ℓ0, and capped ℓ1 regularization. We propose three new objective functions that further promote sparsity, the capped ℓ1 regularization with hinge loss, and the ramp loss versions of approximate ℓ0 and capped ℓ1 regularization. We derive difference of convex functions algorithms (DCA) for solving these novel non-convex objective functions. The proposed algorithms are shown to converge in a finite number of iterations to a local minimum. Using simulated data and several data sets from the University of California Irvine (UCI) machine learning repository, we empirically investigate the fraction of features and examples required by the different classifiers.

This paper investigates sufficient conditions for the convergence to zero of the trajectories of linear switched systems. We provide a collection of results that use weak dwell-time, dwell-time, strong dwell-time, permanent and persistent activation hypothesis. The obtained results are shown to be tight by counterexample. Finally, we apply our result to the three-cell converter.

A new performance measurement and management framework based on value and risk is proposed. The proposed framework is applied to the modelling and evaluation of the a priori performance evaluation of manufacturing processes and to deciding on their alternatives. For this reason, it consistently integrates concepts relevant to objectives, activity, and risk in a single framework comprising a conceptual value/risk model, and it conceptualises the idea of value- and risk-based performance management in a process context. In addition, a methodological framework is developed to provide guidelines for the decision-makers or performance evaluators of the processes. To facilitate the performance measurement and management process, this latter framework is organised in four phases: context establishment, performance modelling, performance assessment, and decision-making. Each phase of the framework is then instrumented with state-of-the-art quantitative analysis tools and methods. For process design and evaluation, the deliverable of the value- and risk-based performance measurement and management system (VR-PMS) is a set of ranked solutions (i.e. alternative business processes) evaluated against the developed value and risk indicators. The proposed VR-PMS is illustrated with a case study from discrete parts manufacturing but is indeed applicable to a wide range of processes or systems.

The hub location problem appears in a variety of applications, including airline systems, cargo delivery systems and telecommunication network design. Hub location problems deal with finding the location of hub facilities and the allocation of demand nodes to these located hub facilities. In this paper, a new model for the capacitated single allocation hub covering location problem is presented. Instead of using capacity constraints to limit the amount of flow received by the hubs, the second objective function is introduced to minimize service times in the hubs. The service time in the hubs includes the waiting time of received flows in a queue and the time to get services. Due to the NP-hardness of the problem, a new weight-based multi-objective imperialist competitive algorithm (MOICA) is designed to find near-optimal solutions. To validate the performance of the proposed algorithm, the solutions obtained by the MOICA are compared by the exact solutions of the mathematical programming model.

Direct ink writing (DIW) belongs to extrusion-based three-dimensional (3D) printing techniques. The success of DIW process depends on well-printable ink and optimized process parameters. After ink preparation, DIW process parameters considerably affect the parts' dimensional accuracy, and process parameters optimization for dimensional accuracy of printed layers is necessary for quality control of parts in DIW. In this study, DIW process parameters were identified and divided into two categories as the parameters for printing a line and the parameter from lines to a layer. Then, a two-step method was proposed for optimizing process parameters. Step 1 was to optimize process parameters for printing a line. In Step 1, continuity and uniformity of extruded filaments and printed rectangular objects were observed in screening experiments to determine printability windows for each process parameter. Then, interaction effect tests were conducted and degree of freedom for experiments was calculated followed by orthogonal array selection for the Taguchi design. Next, main experiments of line printing based on the Taguchi method were conducted. Signal-to-noise ratio calculations and analysis of variance were performed to find the optimal combination and evaluate the significance, respectively. Step 2 was to optimize the parameter from lines to a layer. In Step 2, the average width of the printed line under optimal condition was first measured. Then, single-factor tests of rectangular object printing were conducted to find the optimal parameter from lines to a layer. After these two steps, confirmation results were conducted to verify the reliability of the proposed method and the method robustness on other shapes and other materials; parameter adaptability in 3D parts printing from printed layers' analyses for the proposed method; and parameter adaptability in constructs fabricated as 100% infill or with porosities.

This article presents a new multi-objective model for a facility location problem with congestion and pricing policies. This model considers situations in which immobile service facilities are congested by a stochastic demand following M/M/m/k queues. The presented model belongs to the class of mixed-integer nonlinear programming models and NP-hard problems. To solve such a hard model, a new multi-objective optimization algorithm based on a vibration theory, namely multi-objective vibration damping optimization (MOVDO), is developed. In order to tune the algorithms parameters, the Taguchi approach using a response metric is implemented. The computational results are compared with those of the non-dominated ranking genetic algorithm and non-dominated sorting genetic algorithm. The outputs demonstrate the robustness of the proposed MOVDO in large-sized problems.

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Uncertainty is ubiquitous in tolerance analysis problem. This paper deals with tolerance analysis formulation, more particularly, with the uncertainty which is necessary to take into account into the foundation of this formulation. It presents: a brief view of the uncertainty classification: Aleatory uncertainty comes from the inherent uncertain nature and phenomena and epistemic uncertainty comes from the lack of knowledge a formulation of the tolerance analysis problem based on this classification its development: Aleatory uncertainty is modeled by probability distributions while epistemic uncertainty is modeled by intervals; Monte Carlo simulation is employed for probabilistic analysis while nonlinear optimization is used for interval analysis.

This brief demonstrates a method of real-time motion control for robotic friction stir welding (FSW). For some manufacturing processes, the lack of stiffness of industrial manipulators can cause a lack of precision and is, thus, problematic. During the processes that require significant forces, this error becomes the primary source of defects. This brief provides significant improvements using digital estimators. A compensator based on a discrete-time nonlinear observer and two other compensators that use only motor current and position measurements are proposed to compensate for the tracking error due to the deflection of the robot. Simulations and experiments on an industrial robot show the effectiveness of the three proposed compensators, which successfully attenuate the dynamic error in the case of a 2-D FSW process. Our adapted compensators provide accurate performance (~90% error reduction) for a robotized FSW welding setup.

During recent decades, there has been growing awareness of human safety in the design process. The purpose of this paper is to review the literature on design for human safety (DfHS) in manufacturing systems. To this end, a process for systematically reviewing DfHS studies was used. The authors focused in particular on the applications of design theories and methodologies (DTM) and design tools and techniques (DTT) to analyse and identify work situations in order to improve human safety in manufacturing system design. The authors also tried to identify the design phases in which these DTM and DTT could be applied. This research review covered papers published between 1980 and 2015, and combined seven groups of terms: DfHS, design, safety, DTM, DTT, risk and working situation. A critical analysis was also performed in view to defining a research agenda and the most prominent key actions capable of pointing out paths for future research.

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is maladjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in maladjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.