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The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

Metal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.

The combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.

The contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.

The results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.

The originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.

The purpose of this paper is to present a new method to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V).

The desirability function analysis (DFA), fuzzy set theory with trapezoidal membership function and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method are used to optimize the electro chemical machining process parameters for titanium alloy (Ti6Al4V). In recent years, the utilization of titanium and its alloys, especially of Ti6Al4V materials, in many different engineering fields has undergone a tremendous increase. The ECM process has a potential in the machining of Ti6Al4V. The machining parameters such as electrolyte concentration, current, applied voltage and feed rate with multiple responses such as material removal rate (MRR) and surface roughness (SR) are considered. Experimental work is carried out on Ti6Al4V using second order central composite rotatable design. The two responses are converted into global knit quality index using DFA. Fuzzy set theory with trapezoidal membership function is used to convert all machining parameters and responses into fuzzy values. Then a TOPSIS approach which determines the optimal machining parameters in terms of higher closeness coefficient is proposed to optimize the machining parameters of ECM for titanium alloy. Finally, ANOVA is performed to investigate the significance of each machining parameter and to identify the most influencing factor which affects the process responses.

The optimal machining parameters for ECM process are determined using desirability function analysis, fuzzy set theory and TOPSIS.

A new method is proposed to optimize the electro chemical machining process parameters for titanium alloy.