PREDICTION OF SOLIDIFICATION MODE IN SUPER AUSTENITIC STAINLESS STEEL WELDS
| Author(s) | : | P.K.Nanavati, Prof. B.J.Chauhan, Prof. Dr. Sanjay N. Soman |
| Institution | : | Assistant Professor,Metallurgy Department, Government Engineering College, Gandhinagar, Gujarat |
| Published In | : | Vol. 5, Issue 2 — February 2018 |
| Page No. | : | 201-209 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Super Austenitic OR Nickel alloy. Group of stainless steels having Fe- Ni-Cr-Mo alloys contents. The bestknown material : 904L (20Cr,25Ni,4.5Mo) offers Superior corrosion resistance providing they are welded carefully withlow heat input (less than 1 KJ/mm recommended) and fast travel speeds with no waving.[1](3)(5) This is because of thereason that fusion welding of SASS often destroy the chemical homogeneity of the weld metal composition by developingunavoidable micro segregation of the tramped elements in the solidified weld structure. Which leads to poor corrosion &Mechanical properties[1]. This has been discovered through a research study. Source [1] done about the influence ofMolybdenum on the solidification mode of high Mo bearing, Fe- Ni-Cr-Mo alloys, although SASS fusion welding bestpractices recommending, each run of weld , not to be started until the metal temperature falls below 100ºC. But a nonuniform distribution of alloying elements always remains a possibility. As It has been already discovered by theresearchers[1] that various solidification mode (A, F, AF, FA) and solid state phase transformations will not be a onlyfunction of Cr eq/Nieq but also Mo concentration, specifically due to the transformation of ferrite into eutectoid γ + σ inhigh-Mo alloys. So, it becomes very necessary to understand the possible solidification mode and the very effect of variouselements on various solidification modes.This problem can be overcome by Neural Network analysis, as through well trained model, it is also possible toestablish the relationships between the elements & the different transformation products. The Neural Network classificationmethod has been approached to solve this problem. The database collected from the research paper has been used to develop& train the Probabilistic generalized classification Neural Network (PNN) model to meet the overall objective of predictionof the multifaceted solidification mode of SASS alloys in appropriate welding process as a function of chemical composition,in order to understand the mechanical and corrosive properties of the weld material for use in service applications.
P.K.Nanavati, Prof. B.J.Chauhan, Prof. Dr. Sanjay N. Soman, “PREDICTION OF SOLIDIFICATION MODE IN SUPER AUSTENITIC STAINLESS STEEL WELDS”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 2, pp. 201-209, February 2018.








