Dependence of computed trajectory on step-size in a nonlinear dynamic system: an investigation into cutting tool dynamics

The nonlinear model of an orthogonal cutting process devised by Grabec is studied to demonstrate the importance of choosing an appropriate step-size and evolutionary algorithm for numerically integrating a system of nonlinear differential equations. Two different algorithms are used for computing the system trajectory, and the dependence of the phase portrait on the step-size is shown. A qualitative understanding to connect the proper step-size and the system nonlinearity is established.     

A flammability performance comparison between synthetic and natural clays in polystyrene nanocomposites

Polymer‐clay nanocomposites are a newer class of flame retardant materials of interest due to their balance of mechanical, thermal and flammability properties. Much more work has been done with natural clays than with synthetic clays for nanocomposite flammability applications. There are advantages and disadvantages to both natural and synthetic clay use in a nanocomposite, and some of these, both fundamental and practical, will be discussed in this paper. To compare natural and synthetic clays in regards to polymer flammability, two clays were used. The natural clay was a US mined and refined montmorillonite, while the synthetic clay was a fluorinated synthetic mica. These two clays were used as inorganic clays for control experiments in polystyrene, and then converted into an organoclay by ion exchange with an alkyl ammonium salt. The organoclays were used to synthesize polystyrene nanocomposites by melt compounding. Each of the formulations was analysed by X‐ray diffraction (XRD), thermogravimetric analysis (TGA) and transmission electron microscopy (TEM). Flammability performance was measured by cone calorimeter. The data from the experiments show that the synthetic clay does slightly better at reducing the heat release rate (HRR) than the natural clay. However, all the samples, including the inorganic clay polystyrene microcomposites, showed a decreased time to ignition, with the actual nanocomposites showing the most marked decrease. The reason for this is postulated to be related to the thermal instability of the organoclay (via the quaternary alkyl ammonium). An additional experiment using a more thermally stable organoclay showed a time to ignition identical to that of the base polymer. Finally, it was shown that while polymer‐clay nanocomposites (either synthetic or natural clay based) greatly reduce the HRR of a material, making it more fire safe, they do not provide ignition resistance by themselves, at least, at practical loadings. Specifically, the cone calorimeter HRR curve data appear to support that these nanocomposites continue to burn once ignited, rather than self‐extinguish. Copyright © 2004 John Wiley & Sons, Ltd.     

A program of computer-aided coordination analysis for anundergraduate course in protective relaying

The authors introduce a program used as a tool for protective relaying courses at the undergraduate level. This program allows students to practice protective devices coordination in power systems on a personal computer by representing the time-current curve with dynamic computer graphics. The program is provided with a debugging system that is designed to inform the user of any problem found in a protection scheme. To make students fully understand the dynamic performance of protective devices coordination, the program allows students to assign the location of a fault, and will display the operating time of the protective devices according to the tripping sequence. The program also has the function of automatic coordination analysis, the results of which can be compared with the user's scheme and can also help the user to have a better understanding of what is actually used in industry today     

Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

Electrocardiogram is widely used to diagnose the congestive heart failure (CHF). It is the primary noninvasive diagnostic tool that can guide in the management and follow-up of patients with CHF. Heart rate variability (HRV) signals which are nonlinear in nature possess the hidden signatures of various cardiac diseases. Therefore, this paper proposes a nonlinear methodology, empirical mode decomposition (EMD), for an automated identification and classification of normal and CHF using HRV signals. In this work, HRV signals are subjected to EMD to obtain intrinsic mode functions (IMFs). From these IMFs, thirteen nonlinear features such as approximate entropy \( (E_{\text{ap}}^{x} ) \), sample entropy \( (E_{\text{s}}^{x} ) \), Tsallis entropy \( (E_{\text{ts}}^{x} ) \), fuzzy entropy \( (E_{\text{f}}^{x} ) \), Kolmogorov Sinai entropy \( (E_{\text{ks}}^{x} ) \), modified multiscale entropy \( (E_{{{\text{mms}}_{y} }}^{x} ) \), permutation entropy \( (E_{\text{p}}^{x} ) \), Renyi entropy \( (E_{\text{r}}^{x} ) \), Shannon entropy \( (E_{\text{sh}}^{x} ) \), wavelet entropy \( (E_{\text{w}}^{x} ) \), signal activity \( (S_{\text{a}}^{x} ) \), Hjorth mobility \( (H_{\text{m}}^{x} ) \), and Hjorth complexity \( (H_{\text{c}}^{x} ) \) are extracted. Then, different ranking methods are used to rank these extracted features, and later, probabilistic neural network and support vector machine are used for differentiating the highly ranked nonlinear features into normal and CHF classes. We have obtained an accuracy, sensitivity, and specificity of 97.64, 97.01, and 98.24 %, respectively, in identifying the CHF. The proposed automated technique is able to identify the person having CHF alarming (alerting) the clinicians to respond quickly with proper treatment action. Thus, this method may act as a valuable tool for increasing the survival rate of many cardiac patients.

     

Integration of Derivative Control on SIPIC in Motor Speed Control

The proportional–integral–derivative (PID) controller is the most commonly used controller in control application due to its simplicity. However, the control output may exceed the plant input limit which eventually deteriorates the system performance. This is known as a windup phenomenon, which causes large overshoot, long settling time and instability in the control system. Various anti‐windup methods have been introduced to overcome the windup phenomenon such as the Steady‐State Integral Proportional‐Integral Controller (SIPIC). Due to the coupling of the proportional, integral and derivative tuning gains, it is difficult to tune for non‐overshoot and short settling time to coexist in a PID. With decoupling, a greater range of tuning gains can be applied to control the rising slope without disturbing the damping state. Currently, SIPIC with decoupling effect has only been studied for the proportional and integral tuning gains. This paper presents the effect of integrating derivative control on SIPIC in motor speed control. SIPIC+D shows better speed control on a direct current motor under no‐load and loading conditions compared with other existing anti‐windup added with derivative control.     

Effect of process conditions on the weld‐line strength and microstructure of microcellular injection molded parts

This study was aimed at understanding how the process conditions affect the weld‐line strength and microstructure of injection molded microcellular parts. A design of experiments (DOE) was performed and polycarbonate tensile test specimens were produced for tensile tests and microscopic analysis. Injection molding trials were performed by systematically adjusting four process parameters (i.e., melt temperature, shot size, supercritical fluid (SCF) level, and injection speed). For comparison, conventional solid specimens were also produced. The tensile strength was measured at the weld line and away from the weld line. The weld‐line strength of injecton molded microcellular parts was lower than that of its solid counterparts. It increased with increasing shot size, melt temperature, and injection speed, and was weakly dependent on the supercritical fluid level. The microstructure of the molded specimens at various cross sections were examined using scanning electron microscope (SEM) and a light microscope to study the variation of cell size and density with different process conditions.     

Study of injection molded microcellular polyamide‐6 nanocomposites

This study aims to explore the processing benefits and property improvements of combining nanocomposites with microcellular injection molding. The microcellular nanocomposite processing was performed on an injection‐molding machine equipped with a commercially available supercritical fluid (SCF) system. The molded samples produced based on the Design of Experiments (DOE) matrices were subjected to tensile testing, impact testing, Dynamic Mechanical Analysis (DMA), and Scanning Electron Microscope (SEM) analyses. Molding conditions and nano‐clays have been found to have profound effects on the cell structures and mechanical properties of polyamide‐6 (PA‐6) base resin and nanocomposite samples. The results show that microcellular nanocomposite samples exhibit smaller cell size and uniform cell distribution as well as higher tensile strength compared to the corresponding base PA‐6 microcellular samples. Among the molding parameters studied, shot size has the most significant effect on cell size, cell density, and tensile strength. Fractographic study reveals evidence of different modes of failure and different regions of fractured structure depending on the molding conditions. Polym. Eng. Sci. 44:673–686, 2004. © 2004 Society of Plastics Engineers.     

Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

This article presents an integrated, simulation‐based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn‐around time and adequate accuracy for substituting time‐consuming computer simulations during system‐level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684–694, 2007. © 2007 Society of Plastics Engineers.     

Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering

Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequence approach with an enlarged pheromone-particle table, and (4) global best exchange. These hybrid systems were applied to data clustering. The experimental results utilizing public UCI datasets show that the performances of the proposed hybrid systems are superior compared to those of the K-mean, standalone PSO, and standalone ACOR. Among the four strategies of hybridization, the sequence approach with the enlarged pheromone table is superior to the other approaches because the enlarged pheromone table diversifies the generation of new solutions of ACOR and PSO, which prevents traps into the local optimum.