Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine Learning Article Swipe
Idris Ishak
,
Lee Woon Kiow
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.30880/jamea.2023.04.02.002
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.30880/jamea.2023.04.02.002
Computer-numerical control (CNC) machining has become the norm in most manufacturing businesses. In order to maximize machining efficiency, prevent unintended damage, and maintain product quality all at once, tool wear measurement is essential. Wear on the tools is typically an inevitable side effect of machining. Because it endangers the machining process, flank wear, the most frequent type of tool wear, should be avoided. This study's objective is to fill this growing need by developing a system that can use machine learning to monitor tool condition during the turning process using MATLAB. The regression method with boosted decision trees and SVR with Gaussian kernels are applied to predict the flank wear based on the vibration signal and cutting parameters. This study found that the regression with boosted decision tree method has a lower mean average percentage error, 6.43%, while SVR is 10.11%. Plus, R2 for regression is slightly better than SVR. It shows that the system successfully produced an accurate prediction of flank wear.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.30880/jamea.2023.04.02.002
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390651056Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.30880/jamea.2023.04.02.002Digital Object Identifier
- Title
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Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-14Full publication date if available
- Authors
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Idris Ishak, Lee Woon KiowList of authors in order
- Landing page
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https://doi.org/10.30880/jamea.2023.04.02.002Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.30880/jamea.2023.04.02.002Direct OA link when available
- Concepts
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Flank, Machining, Tool wear, Decision tree, Support vector machine, Computer science, Numerical control, Process (computing), Regression, Artificial intelligence, Mechanical engineering, Machine learning, Engineering, Mathematics, Statistics, Anthropology, Sociology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.value | 0.24131066 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |