MIG welding, also known as Metal Inert Gas welding is being widely used in industries due to its economic advantage. Quality of the welded joint is the main concern, which is a difficult task if input process parameters taken are not proper or well optimized. So, the detailed review of various design of experiments and optimization techniques have been discussed in this paper for obtaining optimal input process parameters. The output parameters such as tensile strength, microhardness, weld pool geometry etc. depend upon the input parameters i.e. wire feed rate, welding current, voltage of arc, gas flow rate, welding speed etc. These parameters are optimized by using various experiment design techniques like Response Surface Methodology, Taguchi, Factorial design. Optimization is done by using Grey Relational Analysis, Artificial Neural Network, Particle Swarm Optimization. Analysis of Variance is used for checking the model adequacy. Mathematical models are generated using regression analysis.
Cite this article:
Sahil Angaria, P. S. Rao, S. S. Dham. Optimization of MIG Welding Process Parameters: A Review. Research J. Engineering and Tech. 2017; 8(3): 273-276. doi: 10.5958/2321-581X.2017.00046.0