Open AccessOptimization of TIG Process Parameter in Improving Mechanical Properties of S304 Stainless Steel Using a Grey Based Taguchi Method
Author(s) -
Naveen Pandey,
Dinesh Dubey
Publication year - 2021
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset218470
Subject(s) - taguchi methods , welding , orthogonal array , materials science , gas tungsten arc welding , design of experiments , mechanical engineering , metallurgy , process variable , grey relational analysis , tungsten , process (computing) , composite material , engineering , computer science , arc welding , mathematics , statistics , mathematical economics , operating system
Tungsten inert gas welding is popular known welding technique for ferrous & nonferrous. Stainless steel grade 3HQ (S30430) is a specialized wire grade with very wide usage for manufacturer of stainless steel fastener. It has now totally replaced Grade 384 and 305 for heading application. The stable austenitic structure makes 302HQ nonmagnetic, even after substantial cold work, and also results in excellent toughness, even down to cryogenic temperatures. This paper attempts in optimizing the Tungsten Inert Gas (TIG) welding process parameter. The effect of various parameters and their influence is important to determine the strength of welded joint. To obtain a good quality weld, it is therefore, essential to control the input welding parameters. Therefore appropriate selection of input welding parameter is necessary in order to obtain a good quality weld and subsequently increase the productivity of manufacturing industry. This paper present multi objective optimization using grey relation analysis (GRA) for S30430 with TIG process to determine the suitable selection of parameters Experiment were conducted according to Taguchi's design of experiments (DOE) with orthogonal array L9 is used, mathematical model was developed using parameters such as speed (mm/min), current (Amp), voltage (V), depth of penetration (mm). After conducting experiment and collecting data, signal to noise ratio were determined by using Minitab18 and it is used to obtain optimum level for every input parameter.