Optimization of Welding Process using a Genetic Algorithm

Abstract

Welding is a complex technological process in which local heating takes place up to the melting point of the connecting and the additional material. Phase and crystallization processes are a strong non-linear function of the cooling rate. This non-linear function multiplies the complexity of the numerical simulation and optimization of the welding process. Lately, optimization with genetic algorithm has become the trend to optimize systems that behave in a non-linear manner and contain a number of local extremes. Genetic algorithm is therefore a method by which we seek an absolute extreme. It is a method which seeks a solution to near absolute extreme. In this paper the use of the genetic algorithm for welding process optimization is described.

The Influence of Material on the Operational Characteristics of Spur Gears Manufactured by the 3D Printing Technology

“…SKF Microlog Analyzer GX was used to collect information on the vibrations (RMS acceleration) of the tested gears [24,26]. A single-axis sensor CMSS 2111 [25,27] was used, which registered the data on the vibration level of the housing of the tested gears in the radial direction and which was positioned in the place indicated in the Fig. 6.…”

Numerical Simulation of Heat Transfer Behavior of Dissimilar AA5052-AA6061 Plates in Fiction Stir Welding: An Experimental Validation

“…In this study a three dimensional thermal model was developed and simulation is made by using commercial FEM code ANSYS. Some numerical simulation works have been done for testifying by some researchers [13][14][15].…”

Determination of Tungsten Inert Gas Welding Input Parameters to Attain Maximum Tensile Strength of 316L Austenitic Stainless Steel

“…From the literature review, Ragavendran et al [6] have studied the effect of the hybrid laser-TIG welding process parameters on weld bead geometry of 316LN stainless steel using desirability approached optimization technique to obtain desired weld bead profile. There are different optimization techniques that can be used to determine the required output responses through the development of mathematical equations to postulate the relationships between the input parameters and output variables [7,8]. One of the most widely used statistical methods of optimization is the response surface methodology (RSM), in which the researchers try to approximate the unknown mechanism with an appropriate empirical model [9,10,11].…”