Optimization of friction stir welding process using NSGA-II and DEMO View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-06-19

AUTHORS

Nizar Faisal Alkayem, Biswajit Parida, Sukhomay Pal

ABSTRACT

In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results. More... »

PAGES

947-956

References to SciGraph publications

  • 2005. DEMO: Differential Evolution for Multiobjective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2014-11-28. Multi objective optimization of friction stir welding parameters using FEM and neural network in INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
  • 1981. Multiple Attribute Decision Making, Methods and Applications A State-of-the-Art Survey in NONE
  • 2016-07-05. Optimization of friction stir welding process parameters using soft computing techniques in SOFT COMPUTING
  • 2012-05-06. Numerical modeling of friction stir welding process: a literature review in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2012-02-17. Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-017-3059-8

    DOI

    http://dx.doi.org/10.1007/s00521-017-3059-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1086100422


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Engineering Mechanics, Hohai University, 210098, Nanjing, Jiangsu, China", 
              "id": "http://www.grid.ac/institutes/grid.257065.3", 
              "name": [
                "Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India", 
                "Department of Engineering Mechanics, Hohai University, 210098, Nanjing, Jiangsu, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Alkayem", 
            "givenName": "Nizar Faisal", 
            "id": "sg:person.011552232313.06", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011552232313.06"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India", 
              "id": "http://www.grid.ac/institutes/grid.417972.e", 
              "name": [
                "Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Parida", 
            "givenName": "Biswajit", 
            "id": "sg:person.013145173313.64", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013145173313.64"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India", 
              "id": "http://www.grid.ac/institutes/grid.417972.e", 
              "name": [
                "Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pal", 
            "givenName": "Sukhomay", 
            "id": "sg:person.07537710321.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07537710321.30"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00170-012-3972-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042499688", 
              "https://doi.org/10.1007/s00170-012-3972-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-31880-4_36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022038375", 
              "https://doi.org/10.1007/978-3-540-31880-4_36"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-48318-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049999080", 
              "https://doi.org/10.1007/978-3-642-48318-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12541-014-0600-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049406569", 
              "https://doi.org/10.1007/s12541-014-0600-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00500-016-2251-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052599789", 
              "https://doi.org/10.1007/s00500-016-2251-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00170-012-4154-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028633073", 
              "https://doi.org/10.1007/s00170-012-4154-8"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-06-19", 
        "datePublishedReg": "2017-06-19", 
        "description": "In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00521-017-3059-8", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1104357", 
            "issn": [
              "0941-0643", 
              "1433-3058"
            ], 
            "name": "Neural Computing and Applications", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "Suppl 2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "31"
          }
        ], 
        "keywords": [
          "evolutionary algorithm", 
          "multi-objective evolutionary algorithm", 
          "artificial neural network model", 
          "neural network model", 
          "back-propagation algorithm", 
          "genetic algorithm II", 
          "network model", 
          "ANN model", 
          "decision-making method", 
          "NSGA-II", 
          "best solution", 
          "parameter settings", 
          "Pareto front", 
          "algorithm II", 
          "differential evolution", 
          "multi-criteria decision-making method", 
          "algorithm", 
          "order preference", 
          "ideal solution", 
          "friction stir welding process", 
          "stir welding process", 
          "demo", 
          "welding process", 
          "process parameter settings", 
          "weld quality", 
          "technique", 
          "process parameters", 
          "solution", 
          "optimal process parameter settings", 
          "optimization", 
          "quality", 
          "good weld quality", 
          "ultimate tensile strength", 
          "model", 
          "process", 
          "quality characteristics", 
          "weld joints", 
          "nugget zone", 
          "mechanical properties", 
          "simulations", 
          "tensile strength", 
          "yield stress", 
          "confirmation experiments", 
          "selection", 
          "work", 
          "simulated results", 
          "similarity", 
          "order", 
          "experiments", 
          "method", 
          "results", 
          "parameters", 
          "preferences", 
          "end", 
          "welds", 
          "setting", 
          "hardness", 
          "characteristics", 
          "comparison", 
          "front", 
          "strength", 
          "evolution", 
          "joints", 
          "elongation", 
          "properties", 
          "angle", 
          "stress", 
          "zone", 
          "correlation"
        ], 
        "name": "Optimization of friction stir welding process using NSGA-II and DEMO", 
        "pagination": "947-956", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1086100422"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00521-017-3059-8"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00521-017-3059-8", 
          "https://app.dimensions.ai/details/publication/pub.1086100422"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-08-04T17:05", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_754.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00521-017-3059-8"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00521-017-3059-8'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00521-017-3059-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-017-3059-8'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-017-3059-8'


     

    This table displays all metadata directly associated to this object as RDF triples.

    168 TRIPLES      21 PREDICATES      99 URIs      85 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00521-017-3059-8 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nfdee57891ae64d8d955f761b7eb73b84
    4 schema:citation sg:pub.10.1007/978-3-540-31880-4_36
    5 sg:pub.10.1007/978-3-642-48318-9
    6 sg:pub.10.1007/s00170-012-3972-z
    7 sg:pub.10.1007/s00170-012-4154-8
    8 sg:pub.10.1007/s00500-016-2251-6
    9 sg:pub.10.1007/s12541-014-0600-x
    10 schema:datePublished 2017-06-19
    11 schema:datePublishedReg 2017-06-19
    12 schema:description In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.
    13 schema:genre article
    14 schema:isAccessibleForFree false
    15 schema:isPartOf N54fba55bfb544c73a49abbb15a07fe61
    16 Na59195846949466a92b7f3d7e47f5eb1
    17 sg:journal.1104357
    18 schema:keywords ANN model
    19 NSGA-II
    20 Pareto front
    21 algorithm
    22 algorithm II
    23 angle
    24 artificial neural network model
    25 back-propagation algorithm
    26 best solution
    27 characteristics
    28 comparison
    29 confirmation experiments
    30 correlation
    31 decision-making method
    32 demo
    33 differential evolution
    34 elongation
    35 end
    36 evolution
    37 evolutionary algorithm
    38 experiments
    39 friction stir welding process
    40 front
    41 genetic algorithm II
    42 good weld quality
    43 hardness
    44 ideal solution
    45 joints
    46 mechanical properties
    47 method
    48 model
    49 multi-criteria decision-making method
    50 multi-objective evolutionary algorithm
    51 network model
    52 neural network model
    53 nugget zone
    54 optimal process parameter settings
    55 optimization
    56 order
    57 order preference
    58 parameter settings
    59 parameters
    60 preferences
    61 process
    62 process parameter settings
    63 process parameters
    64 properties
    65 quality
    66 quality characteristics
    67 results
    68 selection
    69 setting
    70 similarity
    71 simulated results
    72 simulations
    73 solution
    74 stir welding process
    75 strength
    76 stress
    77 technique
    78 tensile strength
    79 ultimate tensile strength
    80 weld joints
    81 weld quality
    82 welding process
    83 welds
    84 work
    85 yield stress
    86 zone
    87 schema:name Optimization of friction stir welding process using NSGA-II and DEMO
    88 schema:pagination 947-956
    89 schema:productId N56ec50f2ef394782a0bde5bb60230bcd
    90 N749876cf86484c81bedcb90f18968843
    91 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086100422
    92 https://doi.org/10.1007/s00521-017-3059-8
    93 schema:sdDatePublished 2022-08-04T17:05
    94 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    95 schema:sdPublisher N9456b36674704f7ba5c9ac16ff07292f
    96 schema:url https://doi.org/10.1007/s00521-017-3059-8
    97 sgo:license sg:explorer/license/
    98 sgo:sdDataset articles
    99 rdf:type schema:ScholarlyArticle
    100 N1e479b3e2d3c45788e80fbf711eec68d rdf:first sg:person.013145173313.64
    101 rdf:rest Nfb11c3cac7d0443e973f9f4de68c6365
    102 N54fba55bfb544c73a49abbb15a07fe61 schema:issueNumber Suppl 2
    103 rdf:type schema:PublicationIssue
    104 N56ec50f2ef394782a0bde5bb60230bcd schema:name dimensions_id
    105 schema:value pub.1086100422
    106 rdf:type schema:PropertyValue
    107 N749876cf86484c81bedcb90f18968843 schema:name doi
    108 schema:value 10.1007/s00521-017-3059-8
    109 rdf:type schema:PropertyValue
    110 N9456b36674704f7ba5c9ac16ff07292f schema:name Springer Nature - SN SciGraph project
    111 rdf:type schema:Organization
    112 Na59195846949466a92b7f3d7e47f5eb1 schema:volumeNumber 31
    113 rdf:type schema:PublicationVolume
    114 Nfb11c3cac7d0443e973f9f4de68c6365 rdf:first sg:person.07537710321.30
    115 rdf:rest rdf:nil
    116 Nfdee57891ae64d8d955f761b7eb73b84 rdf:first sg:person.011552232313.06
    117 rdf:rest N1e479b3e2d3c45788e80fbf711eec68d
    118 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    119 schema:name Information and Computing Sciences
    120 rdf:type schema:DefinedTerm
    121 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    122 schema:name Artificial Intelligence and Image Processing
    123 rdf:type schema:DefinedTerm
    124 sg:journal.1104357 schema:issn 0941-0643
    125 1433-3058
    126 schema:name Neural Computing and Applications
    127 schema:publisher Springer Nature
    128 rdf:type schema:Periodical
    129 sg:person.011552232313.06 schema:affiliation grid-institutes:grid.257065.3
    130 schema:familyName Alkayem
    131 schema:givenName Nizar Faisal
    132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011552232313.06
    133 rdf:type schema:Person
    134 sg:person.013145173313.64 schema:affiliation grid-institutes:grid.417972.e
    135 schema:familyName Parida
    136 schema:givenName Biswajit
    137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013145173313.64
    138 rdf:type schema:Person
    139 sg:person.07537710321.30 schema:affiliation grid-institutes:grid.417972.e
    140 schema:familyName Pal
    141 schema:givenName Sukhomay
    142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07537710321.30
    143 rdf:type schema:Person
    144 sg:pub.10.1007/978-3-540-31880-4_36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022038375
    145 https://doi.org/10.1007/978-3-540-31880-4_36
    146 rdf:type schema:CreativeWork
    147 sg:pub.10.1007/978-3-642-48318-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049999080
    148 https://doi.org/10.1007/978-3-642-48318-9
    149 rdf:type schema:CreativeWork
    150 sg:pub.10.1007/s00170-012-3972-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1042499688
    151 https://doi.org/10.1007/s00170-012-3972-z
    152 rdf:type schema:CreativeWork
    153 sg:pub.10.1007/s00170-012-4154-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028633073
    154 https://doi.org/10.1007/s00170-012-4154-8
    155 rdf:type schema:CreativeWork
    156 sg:pub.10.1007/s00500-016-2251-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052599789
    157 https://doi.org/10.1007/s00500-016-2251-6
    158 rdf:type schema:CreativeWork
    159 sg:pub.10.1007/s12541-014-0600-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049406569
    160 https://doi.org/10.1007/s12541-014-0600-x
    161 rdf:type schema:CreativeWork
    162 grid-institutes:grid.257065.3 schema:alternateName Department of Engineering Mechanics, Hohai University, 210098, Nanjing, Jiangsu, China
    163 schema:name Department of Engineering Mechanics, Hohai University, 210098, Nanjing, Jiangsu, China
    164 Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India
    165 rdf:type schema:Organization
    166 grid-institutes:grid.417972.e schema:alternateName Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India
    167 schema:name Department of Mechanical Engineering, IIT Guwahati, 781039, Guwahati, India
    168 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...