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MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS.

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Title: MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS.
Authors: SHANMUGAM, A.1 (AUTHOR) vickyshanmugam@gmail.com, MOHANRAJ, T.2 (AUTHOR), KRISHNAMURTHY, K.1 (AUTHOR), GUR, ALI KAYA3 (AUTHOR)
Source: Surface Review & Letters. Dec2021, Vol. 28 Issue 12, p1-11. 11p.
Subjects: Abrasive machining, Glass fibers, Plastic fibers, Taguchi methods, TOPSIS method, Surface roughness
Abstract: This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi's L9 orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi's S / N and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5 mm, and traverse speed (TS) of 25 mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers. [ABSTRACT FROM AUTHOR]
Copyright of Surface Review & Letters is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS.
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  Data: <searchLink fieldCode="JN" term="%22Surface+Review+%26+Letters%22">Surface Review & Letters</searchLink>. Dec2021, Vol. 28 Issue 12, p1-11. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Abrasive+machining%22">Abrasive machining</searchLink><br /><searchLink fieldCode="DE" term="%22Glass+fibers%22">Glass fibers</searchLink><br /><searchLink fieldCode="DE" term="%22Plastic+fibers%22">Plastic fibers</searchLink><br /><searchLink fieldCode="DE" term="%22Taguchi+methods%22">Taguchi methods</searchLink><br /><searchLink fieldCode="DE" term="%22TOPSIS+method%22">TOPSIS method</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+roughness%22">Surface roughness</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi's L9 orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi's S / N and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5 mm, and traverse speed (TS) of 25 mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Surface Review & Letters is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1142/S0218625X21501201
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 1
    Subjects:
      – SubjectFull: Abrasive machining
        Type: general
      – SubjectFull: Glass fibers
        Type: general
      – SubjectFull: Plastic fibers
        Type: general
      – SubjectFull: Taguchi methods
        Type: general
      – SubjectFull: TOPSIS method
        Type: general
      – SubjectFull: Surface roughness
        Type: general
    Titles:
      – TitleFull: MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS.
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          Name:
            NameFull: SHANMUGAM, A.
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            NameFull: MOHANRAJ, T.
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            NameFull: KRISHNAMURTHY, K.
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            NameFull: GUR, ALI KAYA
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            – D: 01
              M: 12
              Text: Dec2021
              Type: published
              Y: 2021
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              Value: 28
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              Value: 12
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