Laboratoire de Conception Fabrication Commande

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

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.

Cold metal transfer (CMT)-based wire-arc additive manufacturing (WAAM) is a promising method for the production of large-scale and complex metallic parts because of its high efficiency, less heat input and low cost. However, a critical and common problem with the arc welding processes is the irregular geometry at the beginning and end parts of the bead due to the ignition and extinction of the arc. Based on experimental investigations of the irregularities and different possible optimization methods, an improvement strategy consisting of configurations with a varying travel speed and an extra return path is presented in this paper. Experimental results show that this strategy can effectively enhance the geometric accuracy at the beginning and end parts of different single beads. In the manufacturing of a thin-wall part and a multi-pass cladding, the improvement of geometric accuracy has also been achieved by this strategy.

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.

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.

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.

Cold metal transfer (CMT)–based wire-arc additive manufacturing (WAAM) is increasingly popular for the production of large and complex metallic components due to its high deposition rate, low heat input, and excellent material efficiency. The accurate prediction of the bead geometry is of great importance to enhance the stability of the process and its dimensional accuracy. Besides the wire feed speed (WFS) and travel speed (TS), the interlayer temperature is another key factor in determining the bead geometry because of the heat accumulation in the multilayer deposition. In this paper, considering the varying interlayer temperature, WFS, and TS as inputs, an artificial neural network (ANN) is developed to predict the bead width, height, and contact angle; then, by connecting the ANN model with a bead geometric model, a combined model is established to improve the ANN model. Based on experimental test data, with random combinations of input parameters, the combined model is demonstrated to be able to accurately predict the bead geometry (mean error < 5.1%). The general effect of interlayer temperature on the bead geometry was also investigated by experiment.

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.

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.

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.

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.

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.

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.

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.

International audience

In this paper, a two-stage robust optimisation is presented for an uncapacitated hub location problem in which demand is uncertain and the level of conservatism is controlled by an uncertainty budget. In the first stage, locations for establishing hub facilities were determined, and allocation decisions were made in the second stage. An accelerated Benders decomposition algorithm was used to solve the problem. Computational experiments showed better results in terms of number of iterations and computation time for Benders decomposition with Pareto-optimal cuts in comparison with the classical Benders decomposition algorithm. According to numerical analysis, it was concluded that increasing the uncertainty budget also increased total costs for more established hubs. To determine the uncertainty budget in an appropriate manner, a new expected aggregate function was introduced. The numerical studies demonstrated the usefulness of the proposed method in defining the appropriate uncertainty budget in the presence of uncertainty.