*Student, Department of CAD/CAM Engineering, Central Institute of Plastic Engineering and Technology, Chennai
**Assistant Professor, Department of Mechanical Engineering, University college of Engineering, Dindugal Campus, Tamil Nadu, India
***Assistant Professor, Department of Mechanical Engineering, University college of Engineering, Ramanathapuram Campus, Tamil Nadu, India
Online published on 10 March, 2016.
In friction welding, the joints are formed in the solid state by utilizing the heat generated by friction. The objective of this study an obtaining friction welding of super duplex stainless steel(UNS S32760) joint were investigated considering four process parameters: The process parameters such as friction pressure, upset pressure, burn off length and speed play the major roles in determining the strength of the joints. Optimizing the friction welding parameters in order to establish the weld quality. Similar specimens were joined using the laboratory model friction welding machine. The processed joints were tested for their shrinkage and strength related aspects. Acoustic emission emanated by the joints during tensile testing was acquired to assess the quality of the joints. Also a method to decide near optimal settings of the process parameters using In the present work, a design of experiment (DOE) technique, GENETIC ALGORITHM and artificial neural network (ANN) is proposed minimize shrinkage and maximize tensile strength The optimization procedure resulted in the creation of nondominated optimal points which gave an insight regarding the optimal operating conditions of the process. GA and ANN approach and compare the results obtained.
Friction welding, Genetic algorithm, Multi objective optimization, Tensile strength, Shrinkage, ANN
