Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion Article Swipe
Suiyan Shang
,
Chunjin Wang
,
Xiaoliang Liang
,
Chi Fai Cheung
,
Pai Zheng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/mi14112016
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/mi14112016
This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning machine method. By providing abundant machining information, the machining parameters and force signal data are fused on the feature level to further improve ELM prediction accuracy. An ultra-precision milling experiment was designed and conducted to verify our proposed data-fusion-based ELM method. The results show that the ELM with data fusion outperforms other state-of-art methods in surface roughness prediction. It achieves an impressively low mean absolute percentage error of 1.6% while requiring a mere 18 s for model training.
Related Topics
Concepts
Extreme learning machine
Machining
Surface roughness
Fusion
Artificial intelligence
Sensor fusion
Computer science
Feature (linguistics)
Surface finish
Mean absolute error
Machine learning
Pattern recognition (psychology)
Algorithm
Artificial neural network
Materials science
Engineering
Mean squared error
Mechanical engineering
Mathematics
Statistics
Philosophy
Composite material
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/mi14112016
- https://www.mdpi.com/2072-666X/14/11/2016/pdf?version=1698655101
- OA Status
- gold
- Cited By
- 9
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388020404
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388020404Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/mi14112016Digital Object Identifier
- Title
-
Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data FusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-29Full publication date if available
- Authors
-
Suiyan Shang, Chunjin Wang, Xiaoliang Liang, Chi Fai Cheung, Pai ZhengList of authors in order
- Landing page
-
https://doi.org/10.3390/mi14112016Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-666X/14/11/2016/pdf?version=1698655101Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://www.mdpi.com/2072-666X/14/11/2016/pdf?version=1698655101Direct OA link when available
- Concepts
-
Extreme learning machine, Machining, Surface roughness, Fusion, Artificial intelligence, Sensor fusion, Computer science, Feature (linguistics), Surface finish, Mean absolute error, Machine learning, Pattern recognition (psychology), Algorithm, Artificial neural network, Materials science, Engineering, Mean squared error, Mechanical engineering, Mathematics, Statistics, Philosophy, Composite material, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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