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The in-progress research effort is to optimize the gas metal arc welding parameters on the
impact strength, flexural strength, and hardness of the fused generated part of SS316L using
the Taguchi-based grey relational technique. Due to its low cost and great efficiency,
experiments have been done in conjunction with fused deposition modeling (FDM) as a viable
metal additive manufacturing technology. To create the laminated component, the metal AISI
316L wire is heated, extruded, and other techniques are used. However, it is commonly
assumed that FDMS jobs have scanty and anisotropic tensile properties, which are always
attributed to the weak inter-layer amalgamation. The current study seeks to improve the tensile
characteristics and gain a better understanding of the anisotropic fracture behavior of 316L
stainless steel fabricated via FDMS. It is possible to perceive the surface defect of weld joints
made of 316L austenitic stainless steel. MIG welding parameters, gap between base metal and
nozzle, voltage, and arc current were altered up to three stages, and their special effects on
tensile strength, hardness, tensile strength, surface roughness, and microstructure were
determined. Some parameters are controllable by the machinist, and these parameters
encompass a shortest or indirect collision where the microstructure and mechanical attributes
of the weld meet. In this study, three MIG welding attributes, arc current, voltage, and gap
between base metal and nozzle, were changed up and down to three levels, and their special
effects on surface roughness, hardness, tensile strength, and microstructure were investigated.
Experiments were passed out on a 3 mm thick plate of austenitic stainless steel AISI 316L
exploring MIG welding apparatus and were premeditated according to Taguchi L9 orthogonal
array (OA). Experimentation was run out two times using two unlike filler materials, ER 316L
and ER 309L. For each solo response, input parameters were determined, and the most
favorable parameters were acquired. Additionally, the signal-to-noise (S/N) ratio and analysis
of variance (ANOVA were used to assess the data. The MINI Tab software was used to
optimize the results. It was found that each input parameter influences the reaction. The
adjustment of filler material had manipulated the response, i.e., mechanical attributes and
surface roughness. The microstructure assessment shows that dendritic lathy δ-ferrite
structure,” fine and coarse δ-ferrite structure are nearby in the weld region of various jobs.