Process control based on pattern recognition for routing carbon fiber reinforced polymer

Carbon fiber reinforced polymer (CFRP) is an important composite material. It has many applications in aerospace and automotive fields. The little information available about the machining process of this material, specifically when routing process is considered, makes the process control quite difficult. In this paper, we propose a new process control technique and we apply it to the routing process for that important material. The measured machining conditions are used to evaluate the quality and the geometric profile of the machined part. The machining conditions, whether controllable or uncontrollable are used to control part accuracy and its quality. We present a pattern-based machine learning approach in order to detect the characteristic patterns, and use them to control the quality of a machined part at specific range. The approach is called logical analysis of data (LAD). LAD finds the characteristic patterns which lead to conforming products and those that lead to nonconforming products. As an example, LAD is used for online control of a simulated routing process of CFRP. We introduce the LAD technique, we apply it to the high speed routing of woven carbon fiber reinforced epoxy, and we compare the accuracy of LAD to that of an artificial neural network, since the latter is the most known machine learning technique. By using experimental results, we show how LAD is used to control the routing process by tuning autonomously the routing conditions. We conclude with a discussion of the potential use of LAD in manufacturing.     

A Qualitative Approach to Syllogistic Reasoning

In this paper we present a new approach to a symbolic treatment of quantified statements having the following form Q A's are B's, knowing that A and B are labels denoting sets, and Q is a linguistic quantifier interpreted as a proportion evaluated in a qualitative way. Our model can be viewed as a symbolic generalization of statistical conditional probability notions as well as a symbolic generalization of the classical probabilistic operators. Our approach is founded on a symbolic finite M-valued logic in which the graduation scale of M symbolic quantifiers is translated in terms of truth degrees. Moreover, we propose symbolic inference rules allowing us to manage quantified statements.     

Adaptive Control of a Single Link Manipulator Including Motor and Input Unmodelled Dynamics

An adaptive nonlinear control law that incorporates the manipulatordynamics as well as dynamics of the actuator is developed in this article.The technique is based on nonlinear feedback linearization. The electricalparameters of the actuator are considered to be of uncertain values. Incontrast to known methods the robot position is the only measurementavailable, a nonlinear observer is designed to estimate the remaining statesrequired by the nonlinear controller. Moreover the input unmodelled dynamicsproblem is addressed in the context of nonlinear geometric designs.     

Thermomechanical modeling of polymer nanocomposites by the asymptotic homogenization method

There is much current interest to incorporate nano-scale fillers into polymer matrices to achieve potentially unique properties. Compared with traditional microcomposites, a nanocomposite has a significant large ratio of interface area to volume that results in improved thermomechanical properties. Desired thermomechanical properties of polymer nanocomposites, to achieve the ever-increasing performance requirements, can be obtained by tailoring their microstructures. To this end, computational analyses of the relations between the thermomechanical properties, e.g., Young’s modulus, shear modulus, Poisson’s ratio, yield strength, coefficient of thermal expansion and coefficient of thermal conductivity, in different directions and the microstructures of polymer nanocomposites are performed. The asymptotic homogenization method based on the finite element analysis is used to model the thermomechanical behaviors of different polymer nanocomposites with periodic microstructures. The effects of adding silica, rubber, and clay nanoparticles to epoxy resin as a polymer matrix are analyzed. Mixtures of the nano-particles which differ in volume fraction, material type, size and/or geometry are considered. Some predictions of the thermomechanical properties are compared with experimental data in order to verify the applied modeling technique as an effective design tool to tailor optimal microstructures of polymer nanocomposites.     

Nonisothermal crystallization and melting behavior of PP/nanoclay/CaCO3 ternary nanocomposite

Nonisothermal crystallization and melting behavior of PP/nanoclay/CaCO₃ ternary nanocomposite were investigated using different melt flow index (MFI) of PP, nanoclay and CaCO₃ contents. The rate of crystallization was also studied using relative crystallinity as a function of temperature and time. The results show that the increase of MFI of PP and CaCO₃ content in the prepared ternary nanocomposite shift the crystallization curve of PP to the higher temperature. However, increasing the content of nanoclay from 2 wt % to 6 wt % decreases the crystallization temperature possibly due to the restriction of molecular chain mobility. Further analysis of nonisothermal crystallization was carried out based on Avrami equation which the crystallization kinetic of prepared nanocomposite was evaluated. Except the significant variation in the heat of melting, the influence of these parameters on the melting behavior was much less than the crystallization process. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Experimental validation of doubly fed induction machine electrical faults diagnosis under time-varying conditions

This paper investigates a new diagnosis technique for incipient electrical faults in doubly fed induction machine for wind power systems under time-varying conditions. The proposed method is based on currents frequency sliding pre-processing, and discrete wavelet transform thereby. The mean power calculation of wavelet signals, at different resolution levels, is introduced as a dynamic fault indicator for quantifying the fault extents. The approach effectiveness is proved for both stator and rotor faults under speed and fault varying conditions. Simulation and experimental results show the validity of the developed method, leading to an effective diagnosis procedure for stator and rotor faults in doubly fed induction machines.     

Numerical and experimental study on the effect of solid particle sphericity on cyclone pressure drop

The continuous flow inside cyclone separator is usually simulated by solving the Reynolds averaged Navier–Stokes equations in Eulerian reference frame whereas the dispersed phase is modeled using Lagrangian approach. Although these methods have had a remarkable success, more advanced ideas are needed to model particulate phase in cyclones, especially the non-spherical shaped particles. Numerical simulation is verified with experimental results for the gas-solid flow in a cyclone separator. Reynolds Averaged Navier–Stokes equations (RANS) employing the RNG-based k–ε turbulence model are used to simulate the gas phase. 3-D particle tracking procedure is used for the solid phase. Three different equations for the drag coefficient are utilized in the numerical modeling to acquire more understanding of the behavior of non-spherical particles in cyclones. Computations resulted in the difference of pressure between the inlet and exit of the cyclone, and results are compared with experimental data. Experiments included measuring the separation efficiency of different shapes and sizes of particles. The results indicate that the CFD simulation can effectively reveal the pressure drop behavior as well as separation efficiency of gas-non-spherical particle flow in cyclone.     

A novel flash-like all-metal-oxide semiconductor analog-to-digital converter suitable for system on chips systems

The analog-to-digital converters (ADCs) play a very important role in electronic products, radar, communication systems and signal processing, to name such a few. In this paper, a novel all-metal-oxide semiconductor (MOS) flash-like analog-to-digital converter (FLADC) that consists of five stages is proposed. The design was performed using only MOS transistors, and the proposed ADC works in a way similar to the conventional flash ADC. According to the proposed ADC, there is no need for the comparators used in the conventional flash ADCs, thus resulting in a reduction in both the transistor count and the power consumption. The sound operation and the superiority of the proposed ADC compared to previous works is verified by simulation using the 0.13-μm complementary MOS (CMOS) technology with a power-supply voltage, V_DD, of 1.2 V. The simulation has been conducted on a 5-bit FLADC that is built by 276 MOS transistors only which is approximately 32% of the transistor count of the corresponding conventional flash ADC and has no resistors. According to the simulation results, the proposed 5-bit FLADC consumes 3.23 mW at sampling rate of 0.5 GS/s.