NDLI: A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals

Content Provider	Springer Nature Link
Author	Valiente Bermejo, María Asunción
Copyright Year	2012
Abstract	This paper presents a mathematical model to forecast the level of residual δ ferrite in terms of FN in austenitic stainless steel welds at cooling rates between 10 °C/s up to 10$^{3}$ °C/s. With this aim, two series of austenitic steel specimens were prepared using an electric arc remelt furnace. Whilst the alloying level was kept constant at [Cr$_{eq}$+Ni$_{eq}$] = 30 % and [Cr$_{eq}$+Ni$_{eq}$] = 40 %, the Cr$_{eq}$/Ni$_{eq}$ ratio was gradually increased from 1.22 up to 2.00 in each series. For each alloying level, a highly correlated polynomial function (FN vs. Cr$_{eq}$ /Ni$_{eq}$ ), was found, being Cr$_{eq}$ and Ni$_{eq}$ Hammar and Svensson’s equivalents. These experimental results have led to the importance of [Cr$_{eq}$ +Ni$_{eq}$] and (Cr$_{eq}$ /Ni$_{eq}$ ) variables in the forecast of the residual ferrite content and a general expression including both variables is proposed. $${\rm \bf FN=54.22-126.26(Cr_{eq}+Ni_{eq})+\lbrack-48.11+37.14(Cr_{eq}+Ni_{eq})\rbrack \Big({Cr_{eq}\over Ni_{eq}}\Big)+\lbrack-0.23+61.95(Cr_{eq}+Ni_{eq})\rbrack \Big({Cr_{eq}\over Ni_{eq}}\Big)^2}$$ The proposed model is able to forecast the level of δ ferrite with a mean error of +1.01 FN within a deviation of +/- 2.12 FN with 95 % probability by just considering the chemical composition of the alloy. This level of error has been proved to be lower than DeLong’s and WRC-1988 diagrams errors. Moreover, the proposed model has also been compared with WRC-1992 diagram and FNN-1999 neural network and it provides a more accurate FN forecast within the range of compositions and cooling rates considered.
Starting Page	48
Ending Page	68
Page Count	21
File Format	PDF
ISSN	00432288
Journal	Welding in the World
Volume Number	56
Issue Number	9-10
e-ISSN	18786669
Language	English
Publisher	Springer-Verlag
Publisher Date	2013-03-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Metallic Materials Theoretical and Applied Mechanics Continuum Mechanics and Mechanics of Materials
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Metals and Alloys Mechanical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals

A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals

Effect of Post-Weld Straining on Temper-Rolled Austenitic Stainless Steel Welds

Analysis of the Solidification Process of Austenitic Stainless Steel Weld Metal using Synchrotron Radiation

Effects of Alloying Concepts on Ferrite Morphology and Toughness of Lean Duplex Stainless Steel Weld Metals

Evaluation of austenite reformation in duplex stainless steel weld metal using computational thermodynamics

Microstructures in Friction Stir Welded 304 Austenitic Stainless Steel

Prediction of Ferrite Number in Stainless Steel Welds using Bayesian Neural Network Model

Corrosion Properties in Friction Stir Welded 304 Austenitic Stainless Steel

A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals

Similar Documents

A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals

A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals

Effect of Post-Weld Straining on Temper-Rolled Austenitic Stainless Steel Welds

Analysis of the Solidification Process of Austenitic Stainless Steel Weld Metal using Synchrotron Radiation

Effects of Alloying Concepts on Ferrite Morphology and Toughness of Lean Duplex Stainless Steel Weld Metals

Evaluation of austenite reformation in duplex stainless steel weld metal using computational thermodynamics

Microstructures in Friction Stir Welded 304 Austenitic Stainless Steel

Prediction of Ferrite Number in Stainless Steel Welds using Bayesian Neural Network Model

Corrosion Properties in Friction Stir Welded 304 Austenitic Stainless Steel

A Mathematical Model To Predict δ- Ferrite Content In Austenitic Stainless Steel Weld Metals