Modeling and optimization of ultrasonic metal welding on dissimilar sheets using fuzzy based genetic algorithm approach

Abstract

a b s t r a c tUltrasonic welding has been used in the market over the past twenty years and serving to the manufacturing industries like aviation, medical, microelectronics and many more due to various hurdles faced by conventional fusion welding process. It takes very short time (less than one second) to weld materials, thus it can be used for mass production. But many times, the problems faced by industries due to this process are the poor weld quality and strength of the joints. In fact, the quality and success of the welding depend upon its control parameters. In this present study, the control parameters like vibration amplitude, weld pressure and weld time are considered for the welding of dissimilar metals like aluminum (AA1100) and brass (UNS C27000) sheet of 0.3 mm thickness. Experiments are conducted according to the full factorial design with four replications to obtain the responses like tensile shear stress, T-peel stress and weld area. All these data are utilized to develop a non-linear second order regression model between the responses and predictors. As the quality is an important issue in these manufacturing industries, the optimal combinations of these process parameters are found out by using fuzzy logic approach and genetic algorithm (GA) approach. During experiments, the temperature measurement of the weld zone has also been performed to study its effect on different quality characteristics. From the confirmatory test, it has been observed that, the fuzzy logic yields better output results than GA. A variety of weld quality levels, such as "under weld", "good weld" and "over weld" have also been defined by performing micro structural analysis.

Effects of brazing parameters on the microstructure and tensile shear force of copper sheets using amorphous filler metal

“…In the case of diffusion bonding and diffusion brazing, these techniques create a strong joint but require a high welding temperature and extended welding time, giving rise to the high production cost [8]. The ultrasonic welding process, requires a short joining time but yields low joint strength and is unsuited for high-temperature applications [9][10][11][12].…”

Application of electrical power measurements for process monitoring in ultrasonic metal welding

“…In the study, fuzzy logic has a more accurate output than genetic algorithm. [22] Elangovan, S. et al developed a weld strength analysis model based on welding force, welding time and vibration amplitude using response surface methodology (RSM). The response surface model was further combined with a genetic algorithm to Fig.…”

Parametric Study and Optimization for Welding Processes Using Machine Learning

“…Satpathy M. P. et al [16] analysed and compared the Fuzzy Logic and Genetic Algorithm approach to find the optimal combinations of process parameters for ultrasonic welding of Aluminum and brass for its use in the electrical industry. Vibration amplitude, weld pressure and weld time are considered as control parameters and their impact on tensile shear stress, peel stress and weld area has been investigated using conventional optimization techniques.…”