Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model
A new hybrid model is proposed to address the issue of shrinkage in selective laser sintering parts, which combines the Improved Slime mould Algorithm ( ISMA) and Extreme Learning Machine ( ELM) to predict the shrinkage rate of the parts using limited input data. Firstly, three improvement strategie...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2025-04-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2409 |
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| _version_ | 1849427149535051776 |
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| author | LI Jian NIE Qian JIANG Chenglei GUO Yanling WANG Yangwei |
| author_facet | LI Jian NIE Qian JIANG Chenglei GUO Yanling WANG Yangwei |
| author_sort | LI Jian |
| collection | DOAJ |
| description | A new hybrid model is proposed to address the issue of shrinkage in selective laser sintering parts, which combines the Improved Slime mould Algorithm ( ISMA) and Extreme Learning Machine ( ELM) to predict the shrinkage rate of the parts using limited input data. Firstly, three improvement strategies such as Levy flight, random opposition-based learning and highly disruptive polynomial mutation are used to improve the performance of the viscous bacteria optimization algorithm in all aspects. Subsequently, ISMA is used to optimize the key parameters of ELM, and an ISMA-ELM model is proposed to predict the shrinkage rate of SLS parts. Simulation results demonstrate that the proposed ISMA-ELM obtains optimal prediction results compared to the standard and other algorithm-optimized ELM models. Finally, the optimal processing parameters predicted by the ISMA-ELM model are used to guide the machining, and the dimensional accuracy of the obtained molded parts is improved by 29. 62% compared to the ELM model and 18. 02% compared to the SMA-ELM, which shows that the model can provide optimal process parameters for SLS molding processing and guide the machining effectively. |
| format | Article |
| id | doaj-art-f807dd4e5071496886dcaa42e4f63f6a |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-f807dd4e5071496886dcaa42e4f63f6a2025-08-20T03:29:06ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832025-04-013002112110.15938/j.jhust.2025.02.002Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid ModelLI Jian0NIE Qian1JIANG Chenglei2GUO Yanling3WANG Yangwei4College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaA new hybrid model is proposed to address the issue of shrinkage in selective laser sintering parts, which combines the Improved Slime mould Algorithm ( ISMA) and Extreme Learning Machine ( ELM) to predict the shrinkage rate of the parts using limited input data. Firstly, three improvement strategies such as Levy flight, random opposition-based learning and highly disruptive polynomial mutation are used to improve the performance of the viscous bacteria optimization algorithm in all aspects. Subsequently, ISMA is used to optimize the key parameters of ELM, and an ISMA-ELM model is proposed to predict the shrinkage rate of SLS parts. Simulation results demonstrate that the proposed ISMA-ELM obtains optimal prediction results compared to the standard and other algorithm-optimized ELM models. Finally, the optimal processing parameters predicted by the ISMA-ELM model are used to guide the machining, and the dimensional accuracy of the obtained molded parts is improved by 29. 62% compared to the ELM model and 18. 02% compared to the SMA-ELM, which shows that the model can provide optimal process parameters for SLS molding processing and guide the machining effectively.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2409selective laser sinteringslime mould algorithmextreme learning machinelevy flightrandom opposition-based learninghighly disruptive polynomial mutation |
| spellingShingle | LI Jian NIE Qian JIANG Chenglei GUO Yanling WANG Yangwei Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model Journal of Harbin University of Science and Technology selective laser sintering slime mould algorithm extreme learning machine levy flight random opposition-based learning highly disruptive polynomial mutation |
| title | Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model |
| title_full | Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model |
| title_fullStr | Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model |
| title_full_unstemmed | Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model |
| title_short | Optimization of Selective Laser Sintering Processing Parameters Based on ISMA-ELM Hybrid Model |
| title_sort | optimization of selective laser sintering processing parameters based on isma elm hybrid model |
| topic | selective laser sintering slime mould algorithm extreme learning machine levy flight random opposition-based learning highly disruptive polynomial mutation |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2409 |
| work_keys_str_mv | AT lijian optimizationofselectivelasersinteringprocessingparametersbasedonismaelmhybridmodel AT nieqian optimizationofselectivelasersinteringprocessingparametersbasedonismaelmhybridmodel AT jiangchenglei optimizationofselectivelasersinteringprocessingparametersbasedonismaelmhybridmodel AT guoyanling optimizationofselectivelasersinteringprocessingparametersbasedonismaelmhybridmodel AT wangyangwei optimizationofselectivelasersinteringprocessingparametersbasedonismaelmhybridmodel |