AN APPLICATION OF THE DISCRETE MAXIMUM PRINCIPLE TO THE SEGMENTATION OF A WALL FOR NONLINEAR SLOPES

A problem consisting of the optimal design of a segmented wall on a surface having a nonlinear slope contour is considered. It is formulated as an optimal control problem and a solution procedure based on the Discrete Maximum Principle is developed. An application to a real case, which previously had been solved by a Dynamic Programming based method, is included together with some critical comments on the above-mentioned technique.     

Investigations into the impact of flexibility on manufacturing performance

This paper provides investigative insights into the impact of routeing flexibility, machine flexibility, and product-mix flexibility on the performance of a manufacturing plant. The study employs simulation modelling as the primary tool. The facility modelled is an automobile engine assembly plant consisting of a FMS (flexible manufacturing systems), job shop, and assembly line. A variety of experiments, with the FMS exhibiting one or more of the above three flexibilities at different levels, were simulated on the model. In each experiment the manufacturing performance as given by flow time and work-in-process inventory was tracked. The experiments focused first on the FMS itself, and then on the entire plant. Measures for the three flexibilities are introduced. The simulation results are analysed in detail. The results indicate significant performance benefits in context of the FMS, but little in context of the overall plant     

Block truncation coding with color clumps: A novel feature extraction technique for content based image classification

The paper has explored principle of block truncation coding (BTC) as a means to perform feature extraction for content based image classification. A variation of block truncation coding, named BTC with color clumps has been implemented in this work to generate feature vectors. Classification performance with the proposed technique of feature extraction has been compared to existing techniques. Two widely used public dataset named Wang dataset and Caltech dataset have been used for analyses and comparisons of classification performances based on four different metrics. The study has established BTC with color clumps as an effective alternative for feature extraction compared to existing methods. The experiments were carried out in RGB color space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier were used to measure the classification performances. A paired t test was conducted to establish the statistical significance of the findings. Evaluation of classifier algorithms were done in receiver operating characteristic (ROC) space.     

ROBUSTNESS OPTIMIZATION FOR CONSTRAINED NONLINEAR PROGRAMMING PROBLEMS∗

In realistic situations engineering designs should take into consideration random aberrations from the stipulated design variables arising from manufacturing variability. Moreover, many environmental parameters are often stochastic in nature. Traditional nonlinear optimization attempts to find a deterministic optimum of a cost function and does not take into account the effect of these random variations on the objective. This paper attempts to devise a technique for finding optima of constrained nonlinear functions that are robust with respect to such variations. The expectation of the function over a domain of aberrations in the parameters is taken as a measure of ‘robustness’ of the function value at a point. It is pointed out that robustness optimization is ideally an attempt to trade off between ‘optimality’ and ‘robustness’. A newly-developed multi-criteria optimization technique known as Normal-Boundary Intersection is used to find evenly-spaced points on the Pareto curve for the ‘optimality’ and ‘robustness’ criteria. This Pareto curve enables the user to make the trade-off decision explicitly, free of arbitrary ‘weighting’ parameters.

This paper also formulates a derivative-based approximation for evaluating the expected value of the objective function on the nonlinear manifold defined by the state equations for the system. Existing procedures for evaluating the expectation usually involve numerical integration techniques requiring many solutions of the state equations for one evaluation of the expectation. The procedure presented here bypasses the need for multiple solutions of the state equations and hence provides a cheaper and more easily optimizable approximation to the expectation. Finally, this paper discusses how nonlinear inequality constraints should be treated in the presence of random parameters in the design. Computational results are presented for finding a robust optimum of a nonlinear structural optimization problem.     

Deposition and characterization of diamond-like nanocomposite coatings grown by plasma enhanced chemical vapour deposition over different substrate materials

Diamond-like nanocomposite (DLN) coatings have been deposited over different substrates used for biomedical applications by plasma-enhanced chemical vapour deposition (PECVD). DLN has an interconnecting network of amorphous hydrogenated carbon and quartz-like oxygenated silicon. Raman spectroscopy, Fourier transform–infra red (FT–IR) spectroscopy, transmission electron microscopy (TEM) and X-ray diffraction (XRD) have been used for structural characterization. Typical DLN growth rate is about 1  ${\upmu} $ m/h, measured by stylus profilometer. Due to the presence of quartz-like Si:O in the structure, it is found to have very good adhesive property with all the substrates. The adhesion strength found to be as high as 0·6 N on SS 316 L steel substrates by scratch testing method. The Young’s modulus and hardness have found to be 132 GPa and 14· 4 GPa, respectively. DLN coatings have wear factor in the order of 1 × 10^???7 mm ³ /N-m. This coating has found to be compatible with all important biomedical substrate materials and has successfully been deposited over Co–Cr alloy based knee implant of complex shape.     

An economic design model for X¯ charts with random sampling policies

Recent literature contains many papers on the study of variable (but deterministic) sampling interval (VSI) policies for X¯ control charts. VSI policies perform better than their corresponding classical fixed-sampling-interval (FSI) policies. Results are also available in the literature for an adaptive VSI policy, an enhancement of the VSI policy, where the sample size also changes with the sampling interval. However, for many industrial (especially continuous) processes where X¯ charts are used, maintaining deterministic time intervals between samples is not possible owing to inherent randomness of the processes. In this paper we propose a new variable sampling policy, named random sampling policy (RSP), for X¯ charts in which the sampling intervals and the sample sizes (considered a function of the sampling intervals) are treated as random variables. An economic modeling framework for design of RSP with run rules is developed. Within the proposed modeling framework, special cases of RSP, such as VSI and FSI policies with and without run rules, can also be evaluated. An application of the economic model is demonstrated through design of RSP parameters for two numerical example problems.     

Self-propagating high temperature synthesis (SHS) of titanium carbide

Abstracts are not published in this journal This revised version was published online in November 2006 with corrections to the Cover Date.     

Reduced-graphene-oxide-and-strontium-titanate-based double-layered composite: an efficient microwave-absorbing material

Microwave-absorbing materials based on reduced graphene oxide (r-GO)/strontium titanate were prepared by embedding in epoxy matrix. R-GO and strontium titanate were synthesized and characterized before composite fabrication. Microstructures of the constituent elements were studied by scanning electron microscopy and X-ray diffraction (XRD). Microwave absorption capabilities of the composite absorbers were investigated using a Vector Network Analyser in the range 8–12 GHz. A maximum reflection loss of ?7.5 and ?16.4 dB was obtained at 9.3 and 12.08 GHz, respectively, for 2% (w/w) r-GO-loaded epoxy composites. A maximum attenuation of ?12.8 dB at 9.3 GHz was obtained for the strontium titanate/epoxy composite. However, double-layer composite with r-GO/strontium titanate/epoxy composition showed the maximum reflection loss of ?15.1 dB at 9.47 GHz and ?9.65 dB at 12.3 GHz. All the results are discussed in terms of complex permeability and permittivity. The study revealed that intrinsic conductivity and polarization of the r-GO particles and dielectric polarization of the strontium titanate within epoxy matrix contribute to the microwave absorption.     

Analytical evaluation of DC capacitor RMS current and voltage ripple in neutral-point clamped inverters

The sizing of the DC-link capacitor in a three-level inverter is based on the RMS current flowing through it. This paper analyses the DC-link capacitor RMS current in a neutral-point clamped (NPC) inverter and expresses the same as a function of modulation index, line-side current amplitude and power factor. Analytical closed-form expressions are derived for the capacitor RMS current for single-phase half-bridge, single-phase full-bridge and three-phase three-leg topologies of a three-level inverter. The worst-case capacitor current stress is determined for each topology based on the analytical expressions. Further, analytical expressions are derived for the RMS values of low-frequency and high-frequency capacitor currents. These expressions are then used to estimate voltage ripple across the DC capacitor for sinusoidally modulated three-phase NPC inverter. The analytical expressions for the RMS current and voltage ripple are validated experimentally over a wide range of operating points.