Prediction and Optimization of Parameters Influencing Cold Cracks | Prediction and Optimization of Parameters influencing Cold crack using ANN and Grey Relational Analysis

V. Manivel Muralidaran (u. a.)

ISBN 10: 6203857254 / ISBN 13: 9786203857252

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

Prediction and Optimization of Parameters Influencing Cold Cracks | Prediction and Optimization of Parameters influencing Cold crack using ANN and Grey Relational Analysis | V. Manivel Muralidaran (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203857252 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 120201254

Bibliographic Details

• Title: Prediction and Optimization of Parameters Influencing Cold Cracks | Prediction and Optimization of Parameters influencing Cold crack using ANN and Grey Relational Analysis
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6203857254
• ISBN 13: 9786203857252
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 191 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This textbook describes the content of optimizing and predicting the parameters that influence the cold crack formation in the High Strength Low Alloy Steel 950A. High strength low alloy steel (HSLA) has been in use in workshops since the 1980s. Cold cracking is a common problem associated with welding of HSLA steels. It is thus becoming mandatory to have a novel method of welding to minimize the effects of cold cracking in such steels. The objective of the thesis is to improve the cold cracking resistance of HSLA 950A using the process gas Metal Arc Welding (GMAW). The parameters influencing the cold cracking of HSLA steel are preheating temperature oxide particles content and heat input. These parameters are optimized to achieve high resistance to cold cracking technique using Taguchi and Response surface methodology. The response in this study is taken impact strength. For effectively predict the response using the given input parameters, Artificial Neural Networks (ANN) is used. A three-layer feed-forward back propagation algorithm is used in Ann. Gray Relation Analysis (GRA) technique has been used to perform multi objective optimization.