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    <dc:date>2026-03-23T09:14:29Z</dc:date>
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    <title>Experimental comparison of Friction Stir Welding and Tungsten Inert Gas welding process for dissimilar Aluminium alloy joint</title>
    <link>http://172.22.28.37:8080/xmlui/handle/123456789/1395</link>
    <description>Title: Experimental comparison of Friction Stir Welding and Tungsten Inert Gas welding process for dissimilar Aluminium alloy joint
Authors: Dakave, Urmila
Abstract: Experimental comparison of Friction Stir Welding and Tungsten Inert Gas welding process for dissimilar Aluminium alloy joint
Description: Experimental comparison of Friction Stir Welding and Tungsten Inert Gas welding process for dissimilar Aluminium alloy joint</description>
    <dc:date>2020-01-01T00:00:00Z</dc:date>
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    <title>Multi Objective Optimazation of Process Parameters For CNC Turning of DMR 292 Using Soft Computing</title>
    <link>http://172.22.28.37:8080/xmlui/handle/1/454</link>
    <description>Title: Multi Objective Optimazation of Process Parameters For CNC Turning of DMR 292 Using Soft Computing
Authors: Ghorapade, Vinayak Umesh
Abstract: The performance prediction of the surface finish, cylindricity and dimensional deviation is essential to ensure the quality of a machined work piece. The objective of the current study is to evaluate the functional relationship for the input parameters and the responses, develop the prediction models for the responses and the optimization of the output responses for CNC turning of DMR 292 steel. The current work is carried out at Defence Mettalurgical Research Laboratory, Hyderabad. In this current work, the neural network has been used for prediction of  an output with a good accuracy. The study includes a central composite face-centered design methodology to design the test, and variance analysis is applied to check the fitting degree of the prediction model. The Neural-network-based methodology and the Response Surface Methodology are proposed for predicting the surface roughness, dimensional deviation and cylindricity in a CNC turning process. RSM prediction model defines the functional relationship which gives the various outputs for any inputs within the constraint. Thus GA solver gives the global optimized value in given search space.
Description: Under the Supervision of&#xD;
Dr. Sachin K. Patil&#xD;
(Associate Professor, Department of Mechanical Engineering, R.I.T., Rajaramnagar)&#xD;
&amp;&#xD;
Mr. Umashankar Gupta&#xD;
(Scientist D,Defence Metallurgical Research Laboratory, Hyderabad)</description>
    <dc:date>2017-01-01T00:00:00Z</dc:date>
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    <title>Optimization of Machining Parameters for Cylindricity in CNC Turning of HSLA Steel using Response Surface Methodology and Genetic Algorithm</title>
    <link>http://172.22.28.37:8080/xmlui/handle/1/453</link>
    <description>Title: Optimization of Machining Parameters for Cylindricity in CNC Turning of HSLA Steel using Response Surface Methodology and Genetic Algorithm
Authors: Ghorapade, Vinayak Umesh
Abstract: The performance prediction of the surface finish, cylindricity and dimensional deviation is essential to ensure the quality of a machined work piece. The objective of the current study is to evaluate the functional relationship for the input parameters and the responses, develop the prediction models for the responses and the optimization of the output responses for CNC turning of DMR 292 steel. The current work is carried out at Defence Mettalurgical Research Laboratory, Hyderabad. In this current work, the neural network has been used for prediction of  an output with a good accuracy. The study includes a central composite face-centered design methodology to design the test, and variance analysis is applied to check the fitting degree of the prediction model. The Neural-network-based methodology and the Response Surface Methodology are proposed for predicting the surface roughness, dimensional deviation and cylindricity in a CNC turning process. RSM prediction model defines the functional relationship which gives the various outputs for any inputs within the constraint. Thus GA solver gives the global optimized value in given search space.
Description: Under the Supervision of&#xD;
Dr. Sachin K. Patil&#xD;
(Associate Professor, Department of Mechanical Engineering, R.I.T., Rajaramnagar)&#xD;
&amp;&#xD;
Mr. Umashankar Gupta&#xD;
(Scientist D,Defence Metallurgical Research Laboratory, Hyderabad)</description>
    <dc:date>2017-01-01T00:00:00Z</dc:date>
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    <title>Standardization and simulation of Thyrolux welding machine assembly line</title>
    <link>http://172.22.28.37:8080/xmlui/handle/1/452</link>
    <description>Title: Standardization and simulation of Thyrolux welding machine assembly line
Authors: Patil, Mamtadevi Namdeo
Description: Under the Supervision of&#xD;
Dr. Sachin K. Patil&#xD;
(Associate Professor, Department of Mechanical Engineering, R.I.T., Rajaramnagar)&#xD;
&amp;&#xD;
Mr. Waykole M.N.&#xD;
(Production Manager, Ador Welding Ltd., Pune)</description>
    <dc:date>2017-01-01T00:00:00Z</dc:date>
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