Recycling of RP Models by Solution - Casting Technique

Most of the Rapid Prototyping systems process polymeric materials for model making. Increased environmental concerns and waste minimization demands the recycle of these polymeric models. One way is to melt the models and cast it. However, in this method polymer degradation may occur leading to diminished functionality. In the present work a preliminary attempt has been made to reuse the polymer components by dissolving in a suitable solvent and casting the same into the required shape using soft tooling. Acrylonitrile Butadiene Styrene (ABS) polymer components, made by Fused Deposition Modeling (FDM) process were taken for the present studyMechanical Engineerin

Rapid Prototyping for Aerospace Launch Vehicles

Initial studies of the aerodynamic characteristics of proposed launch vehicles can be made more accurately if lower cost, high-fidelity aerodynamic models are available for wind tunnel testing early in design phase. Rapid Prototyping (RP) is an emerging key technology for producing accurate parts directly from CAD models quickly, with little need of human intervention. Use of RP models was studied at the NASA Marshall Space Flight Center (MSFC). It was concluded that RP methods and materials can be used only for preliminary design studies and limited configurations because of the RP material properties that allow bending of models under higher loading conditions. The reported results and analysis were based on wind tunnel balances. These balances give total load on the body. Thus, there is a need for studying the pressure distribution, the wave pattern and the system behavior under high-speed conditions. In order to study the above goals, a blunt nose cone of a launch vehicle/ missile was tested which was made using the solid based RP method FDM, with a Mach number of 2.0. It is concluded that RP models can take the load at the Mach number 2.0 and also can capture the pressure distribution and wave pattern.Mechanical Engineerin

Strain field in doubly curved surface

This paper presents algorithm for development of structural and continuous curved surface into a planar and non planar (radial) shape in 3D space. The development process is modeled by application of strain in certain plane from the curved surface to its planar development. A doubly curved surface has been generated for the purpose of technical studies. Important features of the approach include formulations of the coefficients of first fundamental form, second fundamental form, Gaussian curvature and Serret Frenet curve. The approximate strain field is obtained by solving a constrained linear and nonlinear problem in algorithm

Analysis and synthesis of mechanical error in path-generating linkages using a stochastic approach

A stochastic model of the four-bar, path-generating linkage has been made. Tolerances and clearances have been assumed to be random variables. The mechanical error in the path of a coupler point is analyzed for the three-sigma band of confidence level. For a specified path, the mechanical error depends on the selection of either the original or its cognate mechanism. A synthesis procedure to allocate tolerances and clearances on different members and joints of a linkage so as to restrict the output error in the path of the coupler point within specified limits is developed. The synthesis procedure helps the designer in finding out how much the tolerance or clerance on particular variable is critical in terms of affecting the output error in the path of a coupler point. Results of an illustrative example are given in the paper

Visualization of Indian Classical Music

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Development of a Methodology for Identification of Indian Musical Instruments

In this work, an attempt is made to develop a methodology for Identication of Indian Musical Instruments. Given a digital audio le with mono recording of an Indian Instrument, we identify the instrument played. The approach involves feature extraction from the signal based on Digital Signal Processing techniques. The spectral moments and pitch of the music signal are used as features. The features extracted from the training data are stored in a database for a learning system based on the k-Nearest Neighbor classier (k-NN). The k-NN method uses a priori information from the training data set to estimate posterior probabilities for an unknown data. We implement the same and test our approach for 4 Indian Instruments - Sitar, Sarod, Tabla and Bansuri. A total of 60 les consisting of 15 recordings of each of the 4 instruments were tested. The recognition was as high as 73.33% for the Tabla and as low as 26.67% for the Sitar