The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system.

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Given high energy demands of buildings, developing countries need to be sensitive to the critical rote of building energy efficiency in the fight against climate change. Especially in tropical countries where the thermal flow is strong and the lack of electricity distribution networks is a sad reality. The consolidation of this energy efficiency requires the preservation of nature through a harmony between the building and its environment on one hand and an effective evaluation of energy performance on the other hand. Faced with these challenges, the bioclimatic concept is one of the best alternatives to weave this harmony between the building and its environment. Furthermore a meaningful energy performance assessment of buildings based on the knowledge of capitalization with the experience feedback processes can be used to structure the different phases of implementation of the buildings. Firstly, this article presents the general concept of bioclimatic buildings with emphasis on thermal notions that influence thermal comfort inside a building. Secondly, the effort focuses on identifying non-qualities and factors of discomfort whose resolution helps to improve the energy and environmental performance of buildings. This approach supported by land surveys to interview the building actors and users to collect data favourable or not favourable to energy-performance. These data are then processed for the generation of graphical representations used by methods developed on the basis of knowledge and strategies of bioclimatic concepts. After the capitalized knowledge from experience feedback processes allows us to offer corrective solutions and share best practices to address the identified performance problems.

International audienceThis paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented

Least squares and output error identification algorithms for continuous time systems with unknown time delay operating in open or closed loop.

The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of demands on control, reliability, availability of machine-tools and on the work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring .... The nonlinear behavior of the main components of the machine-tools: the feeddrives and the spindles, makes the estimation of their fault sensitive physical parameters, difficult to do accurately, As the Artificial Neural Networks (ANNs) are able to model nonlinear process, they might be able to model a parameter estimator. We hope to estimate the physical parameters of feed-drives or spindles by a neural estimator. But before trying this, we have tested the ability of ANNs to estimate the physical parameters of a simple system: a DC motor. The results of this test are presented here.

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