Three-dimensional modelling of geometric defaults to optimize a manufactured part setting

In manufacturing systems, it is essential to consider the functional conditions of a manufactured part. These conditions are influenced by the geometric and dimensional constraints imposed by the initial part diagram. To date, one-dimension chains of dimensions and geometric specifications have been used to ensure the manufacture of defect-free parts. This modelling technique, however, is inefficient since parts are three-dimensional. To address this problem, we have developed a three-dimensional geometric defaults modelling process using small deviation torsor (SDT) theory. This three-dimensional modelling allows for the quantification of geometric defaults in order to respect the imposed manufacturing constraints.     

Effect of manufacturing and assembly defects on two-stage gear systems vibration

The modeling of the dynamic behavior of a two-stage gear system is made for a general configuration of wheels location. The excitation is induced by the periodic variation of the mesh stiffness. This case describes the real working of the gearings. The variation of the mesh phasing is related to the number of teeth and also to the wheels location. We developed a plane model of a two-stage gear system. The modal analysis of the system was then treated. The calculation of the dynamic response was done by a step-by-step time integration method (Newmark algorithm). Three cases of wheels location are treated. The solutions are presented both in the frequency domain and in the time domain. Three types of geometric defects of the toothed wheels are introduced in the model: defect of eccentricity, profile error and assembly defect. An analysis of the effects of these defects on the gear system dynamic behavior is then treated.     

Deep learning and case-based reasoning for predictive and adaptive traffic emergency management

An efficient traffic signal control system (TSCS) should not only be reactive to the current traffic but also be predictive by anticipating future traffic disturbances. In this study, we investigate the potential of using convolution neural network (CNN) in detecting emergency cases and forecasting events that can interrupt the traffic flow. Case-based reasoning (CBR) is then exploited to react to detected and forecasted events. We further develop an adapted Reinforcement Leaning (RL) algorithm in building and enhancing the case bases. The proposed system inherits the advantages of CNN, CBR, and RL, which allow detection, prediction, control, evaluation, and learning in a unified framework. To assess the proposed TSCS, we compare our approach with a set of state-of-art algorithms (e.g., multi-agent preemptive case-based reasoning algorithm and multi-agent preemptive longest queue first—maximal weight matching). The proposed TSCS outperforms the benchmarking algorithms through experiments in various traffic scenarios.

     

Feature construction as a bi-level optimization problem

Neural Computing and Applications - Feature selection and construction are important preprocessing techniques in data mining. They allow not only dimensionality reduction but also classification...     

Temperature and frequency dependent dielectric properties of lithium orthophosphate LiBaPO4

The lithium barium orthophosphate LiBaPO₄ compound has been synthesized by the classic ceramic method and characterized by X-ray diffraction (XRD) technique. The electrical conductivity and modulus characteristics of the system have been investigated in the temperature and the frequency range 681–872 K and 200 Hz–1 MHz respectively by means of impedance spectroscopy. The activation energy value of LiBaPO₄ sample is bigger than of the LiMPO₄ (M = Mn, Co, Ni, Fe) compounds. The frequency dependent conductivity of the present system shows the power law feature. Dielectric data were analyzed using complex electrical modulus M* at various temperatures. The peak positions ω_m of the above spectra shift towards higher frequencies with increase in temperature. The above spectra have been characterized in terms of Kohlrausch–Williams–Watts (KWW) relaxation function to understand relaxation behavior. The activation energy responsible for relaxation calculated from the modulus spectra is found to be almost the same as the value obtained from temperature variation of dc conductivity. The electrical modulus and its scaling behavior are also investigated and the relationship between power-law exponent n and stretched exponential exponent β is found.     

Synthesis of acrylic polymer networks by electroinitiated polymerization

A polyurethane oligomer bearing acrylate end groups has been cross-linked at ambient temperature by electroinitiated polymerization. The initiator-free water-based resin was coated onto a metallic plate serving as cathode by electrodeposition or drying, only its tip being dipped into the electrolyte. The curing of the coating in contact with air was followed by infrared spectroscopy and found to proceed efficiently by applying a 100 mA current, with formation of a hard and insoluble polymer. Electroinitiated polymerization can be associated to photoinitiated polymerization to ensure an effective curing of shadowed areas on tridimensional objects protected by UV-cured coatings.     

Surface energy analysis of polyester fibers modified by graft fluorination

The modification of the PET fiber surface to render a higher degree of hydrophobicity by graft fluorination involves a surface layer of only a few nanometers thickness. The relatively low fraction of the modified surface layer presents difficulties in surface analysis. However, surface free energy characterization is of high importance in the evaluation of the degree of water repellency. This article will discuss the advantages of using wettability studies according to the Wilhelmy method to evaluate the degree of surface graft modification and its effects on the wetting properties. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 75: 10–15, 2000