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Optimisation of tool replacement time in the machining process based on tool condition monitoring using the stochastic approach.

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Title: Optimisation of tool replacement time in the machining process based on tool condition monitoring using the stochastic approach.
Authors: Zaretalab, Arash1, Haghighi, Hamidreza Shahabi1 shahabi@aut.ac.ir, Mansour, Saeed1, Sajadieh, Mohsen S.1
Source: International Journal of Computer Integrated Manufacturing. Feb2019, Vol. 32 Issue 2, p159-173. 15p. 3 Diagrams, 11 Charts, 7 Graphs.
Subjects: Machining, Machine tools -- Automatic control, Manufacturing processes, Industrial productivity, Nonconvex programming, Particle swarm optimization, Mathematical optimization, Stochastic analysis
Abstract: The efficient cutting tool replacement policy can decrease the machining costs and increase the productivity. This study focuses on the stochastic approach for tool life modelling in the milling process. In this study by considering the costs of tool condition monitoring methods a hybrid policy was developed based on the reliability function for optimising the tool replacement time. The proposed policy covers seven functional modes assuming discrete and continuous modes. The policy in this paper can provide the better solutions for determining the discrete and continuous time interval of the tool condition monitoring and also tool replacement time in the milling process. The proposed policy is defined in the non-convex space, thus this policy is optimised by a particle swarm optimisation (PSO) algorithm. The mentioned policy was applied in an experimental process using a CNC milling machine to assess its performance. Finally, the effect of different costs is demonstrated using the sensitivity analysis and the outcome of the optimised policies under various conditions are compared together. The results show that the proposed policy can optimise the tool replacement time due to its flexibility in covering the different functional modes efficiently. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computer Integrated Manufacturing is the property of Taylor & Francis Ltd 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: Optimisation of tool replacement time in the machining process based on tool condition monitoring using the stochastic approach.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Integrated+Manufacturing%22">International Journal of Computer Integrated Manufacturing</searchLink>. Feb2019, Vol. 32 Issue 2, p159-173. 15p. 3 Diagrams, 11 Charts, 7 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+tools+--+Automatic+control%22">Machine tools -- Automatic control</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+productivity%22">Industrial productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Nonconvex+programming%22">Nonconvex programming</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The efficient cutting tool replacement policy can decrease the machining costs and increase the productivity. This study focuses on the stochastic approach for tool life modelling in the milling process. In this study by considering the costs of tool condition monitoring methods a hybrid policy was developed based on the reliability function for optimising the tool replacement time. The proposed policy covers seven functional modes assuming discrete and continuous modes. The policy in this paper can provide the better solutions for determining the discrete and continuous time interval of the tool condition monitoring and also tool replacement time in the milling process. The proposed policy is defined in the non-convex space, thus this policy is optimised by a particle swarm optimisation (PSO) algorithm. The mentioned policy was applied in an experimental process using a CNC milling machine to assess its performance. Finally, the effect of different costs is demonstrated using the sensitivity analysis and the outcome of the optimised policies under various conditions are compared together. The results show that the proposed policy can optimise the tool replacement time due to its flexibility in covering the different functional modes efficiently. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Computer Integrated Manufacturing is the property of Taylor & Francis Ltd 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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        Value: 10.1080/0951192X.2018.1550677
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 159
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      – SubjectFull: Machining
        Type: general
      – SubjectFull: Machine tools -- Automatic control
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      – SubjectFull: Manufacturing processes
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      – SubjectFull: Industrial productivity
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      – SubjectFull: Nonconvex programming
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      – SubjectFull: Particle swarm optimization
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      – SubjectFull: Mathematical optimization
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      – SubjectFull: Stochastic analysis
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    Titles:
      – TitleFull: Optimisation of tool replacement time in the machining process based on tool condition monitoring using the stochastic approach.
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            NameFull: Zaretalab, Arash
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            NameFull: Haghighi, Hamidreza Shahabi
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              M: 02
              Text: Feb2019
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              Y: 2019
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