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Sensitivity of Colding tool life equation on the dimensions of experimental dataset

Johansson, D. LU ; Hägglund, Solveig; Bushlya, V. LU and Ståhl, J. E. LU (2017) In Journal of Superhard Materials 39(4). p.271-281
Abstract

In this work, 22 sets of cutting data and tool life for longitudinal turning of steel are analyzed using the Colding equation. When modeling tool life with a limited number of tool performance data points, the model error may be low for these points. Evaluating the model for test points not used when computing the model coefficients may give larger errors for these points. This work proves that the Colding model also provides sufficient precision when modelling data points not being used to create the model, and is therefore a well-functioning instrument for tool life modelling. The results also prove that for the selected data, the precision of the model can be greatly improved when the dimension of the data set is increased from 5 to... (More)

In this work, 22 sets of cutting data and tool life for longitudinal turning of steel are analyzed using the Colding equation. When modeling tool life with a limited number of tool performance data points, the model error may be low for these points. Evaluating the model for test points not used when computing the model coefficients may give larger errors for these points. This work proves that the Colding model also provides sufficient precision when modelling data points not being used to create the model, and is therefore a well-functioning instrument for tool life modelling. The results also prove that for the selected data, the precision of the model can be greatly improved when the dimension of the data set is increased from 5 to 10 data points. Above 13 data points the precision improvements are negligible.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
machining, the Colding equation, tool life, turning
in
Journal of Superhard Materials
volume
39
issue
4
pages
11 pages
publisher
Springer
external identifiers
  • scopus:85029222569
  • wos:000409936100007
ISSN
1063-4576
DOI
10.3103/S1063457617040074
language
English
LU publication?
yes
id
4ac49c30-7816-405f-99b1-33f59cfcafcb
date added to LUP
2017-10-03 10:08:53
date last changed
2018-01-16 13:20:27
@article{4ac49c30-7816-405f-99b1-33f59cfcafcb,
  abstract     = {<p>In this work, 22 sets of cutting data and tool life for longitudinal turning of steel are analyzed using the Colding equation. When modeling tool life with a limited number of tool performance data points, the model error may be low for these points. Evaluating the model for test points not used when computing the model coefficients may give larger errors for these points. This work proves that the Colding model also provides sufficient precision when modelling data points not being used to create the model, and is therefore a well-functioning instrument for tool life modelling. The results also prove that for the selected data, the precision of the model can be greatly improved when the dimension of the data set is increased from 5 to 10 data points. Above 13 data points the precision improvements are negligible.</p>},
  author       = {Johansson, D. and Hägglund, Solveig and Bushlya, V. and Ståhl, J. E.},
  issn         = {1063-4576},
  keyword      = {machining,the Colding equation,tool life,turning},
  language     = {eng},
  month        = {07},
  number       = {4},
  pages        = {271--281},
  publisher    = {Springer},
  series       = {Journal of Superhard Materials},
  title        = {Sensitivity of Colding tool life equation on the dimensions of experimental dataset},
  url          = {http://dx.doi.org/10.3103/S1063457617040074},
  volume       = {39},
  year         = {2017},
}