Abstract: Contactless mechanical components are mechanical sets for conversion of torque/speed, whose gears and moving parts do not touch each other, but rather they provide movement with magnets and magnetic materials that exert force from a certain distance. Magneto-mechanical transmission devices have several advantages over conventional mechanisms: no friction between rotatory elements (no power losses or heat generation by friction so increase of efficiency), no lubrication is needed (oil-free mechanisms and no lubrication auxiliary systems), reduced maintenance (no lubricant so no need of oil replacements), wider operational temperature ranges (no lubricant evaporation or freezing), overload protection (if overload occurs magnet simply slides but no teeth brake), through-wall connection (decoupling of thermal and electrical paths and environmental isolation), larger operative speeds (more efficient operative conditions), ultralow noise and vibrations (no contact no noise generation). All these advantages permit us to foresee in the long term several common industrial applications in which including contactless technology would mean a significant breakthrough for their performance. In this work, we present three configurations of contactless mechanical passive components: magnetic gears, magnetic torque limiters and superconducting magnetic bearings. We summarize the main characteristic and range of applications for each type; we show experimental results of the most recent developments showing their performance.
Abstract: Although many precision fabrication techniques have demonstrated the ability to produce microstructures and micro-devices with sub 100 nm accuracy, we are yet to see a scalable manufacturing process for large-area production. One promising solution to scalable micro- and nanofabrication is thermal roller imprinting. However, existing investigations on thermal roller imprinting revealed poor pattern transfer fidelity, especially for high aspect ratio features. The standard roller imprinting process suffers from the lack of an effective holding and cooling stage so that the adverse effects from the viscoelastic nature of polymers are not managed. To rectify this problem and further improve the production rate, a new extrusion roller imprinting process with a variotherm belt mold is designed, and its prototype was established at a laboratory scale. The process testing results demonstrate that a 30 μm sawtooth pattern can be faithfully transferred to extruded polyethylene film at take-up speeds higher than 10 m/min. The results are promising in that microfeatures or even nanofeatures may be successfully replicated by a robust and scalable industrial process suitable for large-area, continuous production.
Abstract: In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution.
Abstract: In modern industrial plants, process units are strongly cross-linked with eachother, and disturbances occurring in one unit potentially become plant-wide. This can leadto a flood of alarms at the supervisory control and data acquisition system, hiding the originalfault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrackthe disturbance propagation path of the disturbance and to localize the root cause of thefault. Since detecting correlation in the data is not sufficient to describe the direction of thepropagation path, cause-effect dependencies among process variables need to be detected.Process variables that show a strong causal impact on other variables in the process comeinto consideration as being the root cause. In this paper, different data-driven methods areproposed, compared and combined that can detect causal relationships in data while solelyrelying on process data. The information of causal dependencies is used for localization ofthe root cause of a fault. All proposed methods consist of a statistical part, which determineswhether the disturbance traveling from one process variable to a second is significant, and aquantitative part, which calculates the causal information the first process variable has aboutthe second. The methods are tested on simulated data from a chemical stirred-tank reactorand on a laboratory plant.
Abstract: Artificial Intelligence techniques have being applied to many problems in manufacturing systems in recent years. In the specific field of manufacturing scheduling many studies have been published trying to cope with the complexity of the manufacturing environment. One of the most utilized approaches is (multi) agent-based scheduling. Nevertheless, despite the large list of studies reported in this field, there is no resource or scientific study on the performance measure of this type of approach under very common and critical execution situations. This paper focuses on multi-agent systems (MAS) based algorithms for task allocation, particularly in manufacturing applications. The goal is to provide a mechanism to measure the performance of agent-based scheduling approaches for manufacturing systems under key critical situations such as: dynamic environment, rescheduling, and priority change. With this mechanism it will be possible to simulate critical situations and to stress the system in order to measure the performance of a given agent-based scheduling method. The proposed mechanism is a pioneering approach for performance evaluation of bidding-based MAS approaches for manufacturing scheduling. The proposed method and evaluation methodology can be used to run tests in different manufacturing floors since it is independent of the workshop configuration. Moreover, the evaluation results presented in this paper show the key factors and scenarios that most affect the market-like MAS approaches for manufacturing scheduling.