Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/10559
Title: MODELING AND OPTIMIZATION OF RESISTANCE SPOT WELDING OF GALVANIZED STEEL SHEETS
Authors: Boriwal, Lokesh
Keywords: MECHANICAL INDUSTRIAL ENGINEERING;RESISTANCE SPOT WELDING;GALVANIZED STEEL SHEETS;SPOT WELDING
Issue Date: 2011
Abstract: The input process variables for resistance spot welding of galvanized steel sheets were modelled for achieving acceptable welds. The process variables were also studied for the interaction effects on weld nugget, shear tensile and peel strengths. The multiples regression equations of the model were tested for a range of test cases to predict the responses like nugget size, tensile and peel strength. The designed data was further used for response optimization. The predicted optimized input process variables were experimentally verified by comparing the measured and predicted optimized target responses. The methodology adopted in the present investigation indicated adequacy to model, predict and response optimize the input process variables of resistance spot welding process for welding galvanized steel sheets. The application of neuro-fuzzy method (ANFIS) to model resistance spot welding of galvanized steel sheets. A direct search algorithm was implemented in the proposed model for assigning appropriate membership function to each input. The use of direct search algorithm helped in saving time as the manual assigning of membership functions could be avoided. The neuro-fuzzy model proposed in the present investigation is capable enough to predict process responses based on the input process variables with minimum computational time. Resistance spot welding experiments were conducted as per a design matrix to collect data for the training of the proposed network. A set of test case data, with which the network was not trained, was also used for checking the prediction capability of the network. The proposed model worked well and the comparison between predicted and measured test case data indicated well.
URI: http://hdl.handle.net/123456789/10559
Other Identifiers: M.Tech
Research Supervisor/ Guide: Mahapatra, M. M.
Karunakar, D. B.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (MIED)

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