Sensitivity of Colding tool life equation on the dimensions of experimental dataset
(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.
(Less)
- author
- Johansson, D. LU ; Hägglund, Solveig ; Bushlya, V. LU and Ståhl, J. E. LU
- organization
- publishing date
- 2017-07-01
- 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
- project
- Flintstone2020
- language
- English
- LU publication?
- yes
- id
- 4ac49c30-7816-405f-99b1-33f59cfcafcb
- date added to LUP
- 2017-10-03 10:08:53
- date last changed
- 2024-02-13 07:50:35
@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}},
keywords = {{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 = {{https://lup.lub.lu.se/search/files/57746725/Accepted_manuscript_Sensitivity_of_Colding_tool_life_equation_on_the_dimensions_of_experimental_dataset.pdf}},
doi = {{10.3103/S1063457617040074}},
volume = {{39}},
year = {{2017}},
}