Laboratoire de Génie Informatique, de Production et de Maintenance

This paper presents a proposal for a manufacturing upper ontology, aimed to draft a common semantic net in manufacturing domain. Usefulness of ontologies for data formalization and sharing, especially in a manufacturing environment, are first discussed. Details are given about the Web Ontology Language (OWL) and its adequation for ontologies in the manufacturing systems is shown. A concrete proposal named MASON (MAnufacturing’s Semantics ONtology) is presented and two applications of this ontology are exposed: automatic cost estimation and semantic-aware multiagent system for manufacturing.

The primary goal of this paper is to demonstrate a means to prove asymptotically stable walking in an underactuated, planar, five-link biped robot model. The analysis assumes a rigid contact model when the swing leg impacts the ground and an instantaneous double support phase. The specific robot model analyzed corresponds to a prototype under development by the Centre National de la Recherche Scientifique (CNRS), Paris, France. A secondary goal of the paper is to establish the viability of the theoretically motivated control law. This is explored in a number of ways. First, it is shown how known time trajectories, such as those determined on the basis of walking with minimal energy consumption, can be incorporated into the proposed controller structure. Secondly, various perturbations to the walking motion are introduced to verify disturbance rejection capability. Finally, the controller is demonstrated on a detailed simulator for the prototype which includes torque limits and a compliant model of the walking surface, and thus a noninstantaneous double support phase.

The design of reverse logistics and remanufacturing processes and the recovery of end-of-life products have been well-studied in the literature. Quality, reliability, maintenance and warranty for recovered products and the remanufacturing activities that extend their life are integral issues in reverse logistics. This paper reviews recent and relevant literature on these issues in closed-loop supply chains, with a focus on remanufactured or second-hand products. The published literature is first classified into domain areas of research and practice. The wide array of mathematical tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarised and the main research gaps are highlighted.

The goal of industrial revolutions is not only to improve and respond directly to the needs of industry in a productive side fact, but also to improve the standard of living of society and make life easier for customers. Therefore, economic growth should always go hand in hand with each industrial revolution. In this context, the increasing demand for electricity, the non-renewable nature of conventional sources and the rising price of fuel, are considered the constraints of the 21st century and need to be definitively managed in order to meet the constantly increasing demand for electricity from the population in the best possible conditions. Information and Communication Technologies (ICTs) are the foundation on which tomorrow’s innovative solutions are created. However, smart embedded systems and the internet are two major players driving ICT technologies forward. These technologies have impacted several sectors, where we find a crossroads of several industrial sectors: medical, manufacturing, automation, energy and others. The application these technologies in a smart grid framework with the integration of renewable energy sources and used for an optimization and efficiency mission let Internet of Things (IoT) to take the sustainable and industrial character and named Green Industrial Internet of Things (GIIoT). This last, seems to be a promoting axis for the development of future applications. In this paper, we present an overview of the impact of the Internet of Things on the evolution of applications in relation to the different revolutions, an inventory of the different industrial revolutions, an overview of the GIIoT as well as some applications and perspectives.

Abstract In rolling bearing analysis Hertzian contact theory is used to compute local contact stiffness. This theory does not have a closed form analytical solution and requires numerical calculations to obtain results. Using approximations of elliptical functions and with a mathematical study of Hertzian results, an empirical explicit formulation is proposed in this paper and allows us to obtain the dimensions, the displacement, and the contact stress with at least 0.003% precision and it can be applied to a large range of ellipticity of the contact surface.

This article deals with the combined production and maintenance plans for a manufacturing system satisfying a random demand. We first establish an optimal production plan which minimises the average total inventory and production cost. Second, using this optimal production plan, and taking into account the deterioration of the machine according to its production rate, we derive an optimal maintenance schedule which minimises the maintenance cost. A numerical example illustrates the proposed approach, this analytical approach, based on a stochastic optimisation model and using the operational age concept, reveals the significant influence of the production rate on the deterioration of the manufacturing system and consequently on the integrated production/maintenance policy.

One of the most innovative solutions treated in the literature in order to reduce the environmental impact of urban parcel delivery logistics is the use of drones for delivery on the last kilometer. Consequently, nowadays, the primary challenge is essentially related to the drones’ fleet sizing according to its means of support for the urban delivery of parcels. In this paper, we will discuss the issue of dimensioning from a forecast of deliveries of an urban perimeter, the size of the fleet, the stock of battery to dispose of and the strategy of battery charging. We will present an analytical model expressing the proposed problem of the optimal drones’ delivery mission taking into account the issues of autonomy and energy consumption related to the drone’s technical specification. According to the developed analytical model, two optimization policies will be proposed. The first policy consists of planning missions under reducing distance. The second policy tries to make a compromise between the distance and the number of drones. A case study will be presented in order to compare the two policies based on the overall cost of a plan. The main objective of the study is to create a decision-making tool for the design of a drone fleet in the case of forecast deliveries over a time horizon under operational constraints.

This paper proposes an integrated method for preventive maintenance and inventory control of a production line, composed of n machines (n ≥ 1) without intermediate buffers. The machines are subject to failures and an age-dependent preventive maintenance policy is used. Approximate analytical results are proposed for the one machine case. Simulation software is used to model and simulate the behaviour of the production line of n machines under various maintenance and inventory control strategies. A methodology combining the simulation and genetic algorithms is proposed jointly to optimize maintenance and inventory control policies. Results are compared with the analytical solutions.

A novel line balancing problem is considered. It differs from assembly line balancing problems in that the operations of each workstation are partitioned into blocks of simultaneously executed (parallel) operations. The blocks of each workstation are executed sequentially. For the line design stage considered in this paper, the compatibility (inclusion and exclusion) constraints for grouping operations into blocks and workstations as well as precedence constraints are known. The goal is to minimize a weighted sum of the number of workstations and the number of blocks while achieving a desired cycle time and satisfying all the constraints. The developed exact and heuristic methods are based on a mixed-integer programming approach. Experimental results are reported.

This paper addresses the forbidden-state problem of general marked graphs with uncontrollable transitions. The models need not to be safe nor cyclic. Control requirements are expressed as the conjunction of general mutual exclusion constraints (GMEC) of markings of so-called critical places. Structural properties such as influence paths and influence zones are proposed to perform the worst-case analysis for each GMEC specification with any given initial marking when only uncontrollable transitions are allowed. Efficient solutions are proposed for the determination of the maximal uncontrollably reachable marking of any critical place, and this for many possible, more or less general, configurations of the net structure, even for the case of overlapping paths of critical places. Besides, we demonstrate that these results can be easily extended to unbounded nets and when critical places have negative weights. For the most general case, when analytical solution is not available, a linear programming approach is proposed. The great advantage of the proposed approach over existing methods is emphasized by using it to solve the supervisory control problem of the automated manufacturing system of Atelier Inter-etablissements de Productique Rhone Alpes Ouest (AIP-RAO), Lyon, France.

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.

The increase demand for electricity and the non-renewable nature of fossil energy makes the move towards renewable energies required. However, the common problem of renewable sources, which is the intermittence, is overcome by the hybridization of complementary sources. Thus, whenever the load demand is not fully covered by the primary source, the second one will absolutely support it. Furthermore, the production, the interaction with the grid and the storage system must be managed by the grid-connected hybrid renewable energy system, which is the main objective of this paper. Indeed, we propose a new system of a grid-connected PV-battery, which can manage its energy flows via an optimal management algorithm. The DC bus source connection topology in our proposed hybrid architecture tackles the synchronization issues between sources when the load is powered. We consider in this work that choosing a battery discharge and charge limiting power provides an extension of the battery life. On the other hand, we simulated the dynamic behavior of the architecture’s various components according to their mathematical modeling. Following this, an energy management algorithm was proposed, and simulated using MATLAB/SIMULINK to serve the load. The results have shown that the load was served in all cases, taking into account the electrical behavior of the inhabitants as well as the weather changes on a typical day. Indeed, the load was served either by instant solar production between sunrise and sunset, or the recovery from sunset to 10pm, which could be a stored or injected energy without exceeding the 1000W per hour.

This paper investigates the problem of optimally integrating production quality and condition-based maintenance in a stochastically deteriorating single- product, single-machine production system. Inspections are periodically performed on the system to assess its actual degradation status. The system is considered to be in ‘fail mode’ whenever its degradation level exceeds a predetermined threshold. The proportion of non-conforming items, those that are produced during the time interval where the degradation is beyond the specification threshold, are replaced either via overtime production or spot market purchases. To optimise preventive maintenance costs and at the same time reduce production of non-conforming items, the degradation of the system must be optimally monitored so that preventive maintenance is carried out at appropriate time intervals. In this paper, an integrated optimisation model is developed to determine the optimal inspection cycle and the degradation threshold level, beyond which preventive maintenance should be carried out, while minimising the sum of inspection and maintenance costs, in addition to the production of non-conforming items and inventory costs. An expression for the total expected cost rate over an infinite time horizon is developed and solution method for the resulting model is discussed. Numerical experiments are provided to illustrate the proposed approach.

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.

The reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm driven by the high responsiveness and performance efficiencies. In such system, machines, material handling units or machines components can be added, modified, removed or interchanged as needed. Hence, the design of RMS is based on reconfigurable machines capabilities and product specification. This paper addresses the problem of machines selections for RMS design under unavailability constraints and aims to develop an approach to ensure the best process plan according to the customised flexibility required to produce all parts of a given product. More specifically, we develop a flexibility-based multi-objective approach using an adapted version of the well-known non-dominated sorting genetic algorithm to select adequate machines from a set of candidate (potential) ones, in order to ensure the best responsiveness of the designed system in case of unavailability of one of the selected machines. The responsiveness is based on the flexibility of the designed system and a generated process plan, which guarantees the management of machines unavailability. It is defined as the ability and the capacity to adapt the process plan in response to machines unavailability. Two objectives are considered, respectively, the maximisation of the flexibility index of the system and the minimisation of the total completion time. To choose the best solution in the Pareto front, a multi-objective decision-making method called technique for order of preference by similarity to ideal solution is used. To demonstrate the applicability of the proposed approach, a simple example is presented and the numerical results are analysed.

This paper presents a joint optimal inventory control and preventive maintenance strategy for a randomly failing production unit which supplies an assembly line operating according to a just-in-time configuration. The production unit is submitted to a maintenance action as soon as it reaches a certain age T or at failure; whichever occurs first. In order to palliate perturbations caused by breakdowns and by planned maintenance actions both with random durations, a buffer stock is built up to ensure the supply of the assembly line at a constant rate. A mathematical model is developed to evaluate the average cost per time unit of the proposed strategy. Due to the complexity of the mathematical model and to some approximation assumptions, a second approach based on simulation and experimental design is developed. It allows us to simulate the proposed policy and to generate a second order response surface for easy computation of the optimal values of the decision variables which are the age T at which preventive maintenance must be performed, and the buffer stock level h.

International audience

In order to optimize the mechanical behavior of high speed rotors, it is useful to know the load-displacement law of the angular-contact ball bearing. The relationship between preload, speed, and contact angle is studied and a new analytical approach is proposed, giving explicitly and with good precision the contact angle versus preload and rotational speed for the special case of elastically preloaded high speed angular-contact ball bearing.

Industrial performance can be defined in terms of numerous criteria to be synthesized for global control purposes, many of them being of a complex nature, i.e. not related to one elementary physical measure. In this context, determining a global performance expression raises the issue of performance expression aggregation. To address such an aggregation issue, so-called performance measurement systems need to be implemented. The analysis of the corresponding literature leads to the conclusion that the majority of the proposed approaches either do not provide explicit aggregation mechanisms or propose overly simplistic methods really to cope with the complexity of the situations at hand. Only a few consider explicit adequate aggregation methods. Therefore, a thorough characterization of performance expression aggregation based on four stages, namely extraction, representation, combination and interpretation, as proposed by the information aggregation community is presented. The contribution deals with the definition of a performance expression combination based on two kinds of performance expressions commonly encountered (physical measures and performance evaluations, i.e. objective satisfaction degrees). Methodological guidelines for performance expression aggregation are proposed as well as associated mathematical tools, especially the fuzzy Choquet integral for taking criteria interactions into account. An industrial case study is used as an illustration, and some concluding remarks and emerging problems to be considered in the future, such as temporal aggregation, are finally pointed out.

International audience