Prediction of Aluminum Alloy Surface Roughness Through Nanosecond Pulse Laser Assisted by Continuous Laser Paint Removal Article Swipe
Jingyi Li
,
Rui Liang
,
Han Li
,
Junjie Liu
,
Jingdong Sun
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/photonics12060575
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/photonics12060575
Reducing surface roughness can enhance the mechanical properties of processed materials. The variation law of the aluminum alloy surface roughness induced by continuous-nanosecond combined laser (CL) with different continuous laser power densities and laser delay is investigated experimentally. A back propagation neural network (BPNN) coupled with a sparrow search algorithm (SSA) is employed to predict surface roughness. The nanosecond laser energy density, continuous laser power density and laser delay are input parameters, while the surface roughness is output parameter. The lowest surface roughness is achieved with completely paint film removed by the CL while the nanosecond laser energy density is 1.99 J/cm2, the continuous laser power density is 2118 W/cm2 and the laser delay is 1 ms. Compared to the original target and the target irradiated by nanosecond pulse laser (ns laser), the reductions in the surface roughness are 20.62% and 12.00%, respectively. The SSA-BPNN model demonstrates high prediction accuracy, with a correlation coefficient (R2) of 0.98628, root mean square error (RMSE) of 0.024, mean absolute error (MAE) of 0.020 and mean absolute percentage error (MAPE) of 1.30% on the test set. These results indicate that the SSA-BPNN demonstrates higher-precision surface roughness prediction with limited experimental data than BPNN. Furthermore, the findings confirm that the CL can effectively reduce surface roughness.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/photonics12060575
- https://www.mdpi.com/2304-6732/12/6/575/pdf?version=1749205210
- OA Status
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- References
- 30
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.3390/photonics12060575Digital Object Identifier
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Prediction of Aluminum Alloy Surface Roughness Through Nanosecond Pulse Laser Assisted by Continuous Laser Paint RemovalWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-06-06Full publication date if available
- Authors
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Jingyi Li, Rui Liang, Han Li, Junjie Liu, Jingdong SunList of authors in order
- Landing page
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https://doi.org/10.3390/photonics12060575Publisher landing page
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https://www.mdpi.com/2304-6732/12/6/575/pdf?version=1749205210Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2304-6732/12/6/575/pdf?version=1749205210Direct OA link when available
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Materials science, Surface roughness, Alloy, Laser, Aluminium, Nanosecond, Surface finish, Optics, Pulse (music), Optoelectronics, Metallurgy, Composite material, Detector, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2304-6732/12/6/575/pdf?version=1749205210 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Photonics |
| primary_location.landing_page_url | https://doi.org/10.3390/photonics12060575 |
| publication_date | 2025-06-06 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4296714315, https://openalex.org/W4400132906, https://openalex.org/W2590216323, https://openalex.org/W4404006925, https://openalex.org/W3134092815, https://openalex.org/W2319736540, https://openalex.org/W3009704296, https://openalex.org/W2063074379, https://openalex.org/W2798199238, https://openalex.org/W3101947797, https://openalex.org/W2036380748, https://openalex.org/W3019246758, https://openalex.org/W3043518596, https://openalex.org/W2047009169, https://openalex.org/W2045761402, https://openalex.org/W2972644837, https://openalex.org/W2982548824, https://openalex.org/W3196397602, https://openalex.org/W4205862371, https://openalex.org/W4361283428, https://openalex.org/W2490332050, https://openalex.org/W4207013096, https://openalex.org/W3042305584, https://openalex.org/W3111312859, https://openalex.org/W4285384523, https://openalex.org/W3121721182, https://openalex.org/W3096452976, https://openalex.org/W4294176373, https://openalex.org/W3175081510, https://openalex.org/W4281622751 |
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