Tailoring complex weld geometry through reliable heat-transfer and fluid-flow calculations and a genetic algorithm

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Systematic tailoring of weld attributes based on scientific principles is an important goal in fabricating reliable welds. What is needed, and is not currently available, is the ability to systematically determine multiple welding-variable sets to achieve a target weld feature such as geometry. Here, we show how the transport phenomena-based models can be completely restructured to achieve this goal. First, the reliability of the heat-transfer and fluid-flow model predictions is increased by optimizing the values of uncertain input variables such as the arc efficiency from a limited volume of experimental data. Next, after the model predictions are made reliable, the numerical heat-transfer and fluid-flow model is coupled with a genetic algorithm (GA) to achieve bidirectionality of the model and to determine multiple pathways to achieve a specified weld attribute such as the weld geometry. The proposed approach is demonstrated in complex gas metal-arc (GMA) fillet welding of low-alloy steel, for which various sets of welding variables are computed to achieve a specified weld geometry. The model predictions are compared with appropriate independent experimental results. The modeling results, apart from providing definitive insight regarding the complex physics of welding, also provide hope that weld attributes can be tailored reliably through multiple routes based on heat-transfer and fluid-flow calculations and evolutionary algorithms.

Original languageEnglish (US)
Pages (from-to)2725-2735
Number of pages11
JournalMetallurgical and Materials Transactions A: Physical Metallurgy and Materials Science
Volume36
Issue number10
DOIs
StatePublished - Jan 1 2005

Fingerprint

genetic algorithms
fluid flow
Flow of fluids
Welds
Genetic algorithms
heat transfer
Heat transfer
welding
Geometry
geometry
Welding
arcs
predictions
fillets
high strength steels
Gas metal arc welding
High strength steel
Evolutionary algorithms
routes
Physics

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Mechanics of Materials
  • Metals and Alloys

Cite this

@article{83279f9cc1f04ebaa9cfc6b8a475f19e,
title = "Tailoring complex weld geometry through reliable heat-transfer and fluid-flow calculations and a genetic algorithm",
abstract = "Systematic tailoring of weld attributes based on scientific principles is an important goal in fabricating reliable welds. What is needed, and is not currently available, is the ability to systematically determine multiple welding-variable sets to achieve a target weld feature such as geometry. Here, we show how the transport phenomena-based models can be completely restructured to achieve this goal. First, the reliability of the heat-transfer and fluid-flow model predictions is increased by optimizing the values of uncertain input variables such as the arc efficiency from a limited volume of experimental data. Next, after the model predictions are made reliable, the numerical heat-transfer and fluid-flow model is coupled with a genetic algorithm (GA) to achieve bidirectionality of the model and to determine multiple pathways to achieve a specified weld attribute such as the weld geometry. The proposed approach is demonstrated in complex gas metal-arc (GMA) fillet welding of low-alloy steel, for which various sets of welding variables are computed to achieve a specified weld geometry. The model predictions are compared with appropriate independent experimental results. The modeling results, apart from providing definitive insight regarding the complex physics of welding, also provide hope that weld attributes can be tailored reliably through multiple routes based on heat-transfer and fluid-flow calculations and evolutionary algorithms.",
author = "A. Kumar and Tarasankar Debroy",
year = "2005",
month = "1",
day = "1",
doi = "10.1007/s11661-005-0269-y",
language = "English (US)",
volume = "36",
pages = "2725--2735",
journal = "Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science",
issn = "1073-5623",
publisher = "Springer Boston",
number = "10",

}

TY - JOUR

T1 - Tailoring complex weld geometry through reliable heat-transfer and fluid-flow calculations and a genetic algorithm

AU - Kumar, A.

AU - Debroy, Tarasankar

PY - 2005/1/1

Y1 - 2005/1/1

N2 - Systematic tailoring of weld attributes based on scientific principles is an important goal in fabricating reliable welds. What is needed, and is not currently available, is the ability to systematically determine multiple welding-variable sets to achieve a target weld feature such as geometry. Here, we show how the transport phenomena-based models can be completely restructured to achieve this goal. First, the reliability of the heat-transfer and fluid-flow model predictions is increased by optimizing the values of uncertain input variables such as the arc efficiency from a limited volume of experimental data. Next, after the model predictions are made reliable, the numerical heat-transfer and fluid-flow model is coupled with a genetic algorithm (GA) to achieve bidirectionality of the model and to determine multiple pathways to achieve a specified weld attribute such as the weld geometry. The proposed approach is demonstrated in complex gas metal-arc (GMA) fillet welding of low-alloy steel, for which various sets of welding variables are computed to achieve a specified weld geometry. The model predictions are compared with appropriate independent experimental results. The modeling results, apart from providing definitive insight regarding the complex physics of welding, also provide hope that weld attributes can be tailored reliably through multiple routes based on heat-transfer and fluid-flow calculations and evolutionary algorithms.

AB - Systematic tailoring of weld attributes based on scientific principles is an important goal in fabricating reliable welds. What is needed, and is not currently available, is the ability to systematically determine multiple welding-variable sets to achieve a target weld feature such as geometry. Here, we show how the transport phenomena-based models can be completely restructured to achieve this goal. First, the reliability of the heat-transfer and fluid-flow model predictions is increased by optimizing the values of uncertain input variables such as the arc efficiency from a limited volume of experimental data. Next, after the model predictions are made reliable, the numerical heat-transfer and fluid-flow model is coupled with a genetic algorithm (GA) to achieve bidirectionality of the model and to determine multiple pathways to achieve a specified weld attribute such as the weld geometry. The proposed approach is demonstrated in complex gas metal-arc (GMA) fillet welding of low-alloy steel, for which various sets of welding variables are computed to achieve a specified weld geometry. The model predictions are compared with appropriate independent experimental results. The modeling results, apart from providing definitive insight regarding the complex physics of welding, also provide hope that weld attributes can be tailored reliably through multiple routes based on heat-transfer and fluid-flow calculations and evolutionary algorithms.

UR - http://www.scopus.com/inward/record.url?scp=27144432403&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=27144432403&partnerID=8YFLogxK

U2 - 10.1007/s11661-005-0269-y

DO - 10.1007/s11661-005-0269-y

M3 - Article

AN - SCOPUS:27144432403

VL - 36

SP - 2725

EP - 2735

JO - Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science

JF - Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science

SN - 1073-5623

IS - 10

ER -