Optimization framework of laser oscillation welding based on a deep predictive reward reinforcement learning net.
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| Title: | Optimization framework of laser oscillation welding based on a deep predictive reward reinforcement learning net. |
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| Authors: | Tian, Wenhao1 (AUTHOR), Hu, Peipei2 (AUTHOR), Zhang, Chen1 (AUTHOR) c.zhang@whu.edu.cn |
| Source: | Journal of Intelligent Manufacturing. Aug2025, Vol. 36 Issue 6, p4331-4350. 20p. |
| Subjects: | Deep reinforcement learning, Aluminum alloy welding, Laser welding, Machine learning, Production engineering, Reinforcement learning |
| Abstract: | This research proposed a laser oscillation welding optimization system based on a deep reinforcement learning model with neural-network-based reward mechanism. A deep predictive reward reinforcement learning net (DPRRL-net) was developed to predict and improve the quality and efficiency of laser oscillation welding aluminum alloys by optimizing the process parameters, such as laser power, welding speed, oscillation amplitude, and oscillation frequency, with the porosity and penetration as the optimization target. A back propagation neural network (BPNN) prediction model optimized by differential evolutionary algorithm (DE) was established based on experimental results, and a linear weighting method was used to create a comprehensive evaluation system for weld quality and efficiency. The relative error between the predicted and experimental values of the DE-BPNN model was within 2.7%. The combination of reinforcement learning algorithm of twin delayed deep deterministic policy gradient (TD3) and comprehensive welding quality and efficiency prediction model was used to determine the optimal process parameters and avoid local optima or over-fitted solutions. Weld samples under these parameters showed a 7.04% increase in penetration and no porosity compared to traditional algorithm. The results demonstrated that the proposed method can effectively optimize the laser oscillation welding process parameters for aluminum alloys and significantly improve the weld quality and processing efficiency. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186645298 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimization framework of laser oscillation welding based on a deep predictive reward reinforcement learning net. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tian%2C+Wenhao%22">Tian, Wenhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Peipei%22">Hu, Peipei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chen%22">Zhang, Chen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> c.zhang@whu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Aug2025, Vol. 36 Issue 6, p4331-4350. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Aluminum+alloy+welding%22">Aluminum alloy welding</searchLink><br /><searchLink fieldCode="DE" term="%22Laser+welding%22">Laser welding</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Production+engineering%22">Production engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This research proposed a laser oscillation welding optimization system based on a deep reinforcement learning model with neural-network-based reward mechanism. A deep predictive reward reinforcement learning net (DPRRL-net) was developed to predict and improve the quality and efficiency of laser oscillation welding aluminum alloys by optimizing the process parameters, such as laser power, welding speed, oscillation amplitude, and oscillation frequency, with the porosity and penetration as the optimization target. A back propagation neural network (BPNN) prediction model optimized by differential evolutionary algorithm (DE) was established based on experimental results, and a linear weighting method was used to create a comprehensive evaluation system for weld quality and efficiency. The relative error between the predicted and experimental values of the DE-BPNN model was within 2.7%. The combination of reinforcement learning algorithm of twin delayed deep deterministic policy gradient (TD3) and comprehensive welding quality and efficiency prediction model was used to determine the optimal process parameters and avoid local optima or over-fitted solutions. Weld samples under these parameters showed a 7.04% increase in penetration and no porosity compared to traditional algorithm. The results demonstrated that the proposed method can effectively optimize the laser oscillation welding process parameters for aluminum alloys and significantly improve the weld quality and processing efficiency. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10845-024-02465-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 4331 Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Aluminum alloy welding Type: general – SubjectFull: Laser welding Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Production engineering Type: general – SubjectFull: Reinforcement learning Type: general Titles: – TitleFull: Optimization framework of laser oscillation welding based on a deep predictive reward reinforcement learning net. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tian, Wenhao – PersonEntity: Name: NameFull: Hu, Peipei – PersonEntity: Name: NameFull: Zhang, Chen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09565515 Numbering: – Type: volume Value: 36 – Type: issue Value: 6 Titles: – TitleFull: Journal of Intelligent Manufacturing Type: main |
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