Partial polishing of experimental data.
<div><p>In this study, an adaptive force-position-speed collaborative process planning framework for robot polishing was proposed to improve the stability of the robot polishing process. The material removal model based on Preston’s theory was studied, and the factors of polishing pressu...
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2025
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| _version_ | 1852017012348289024 |
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| author | Ma Haohao (22177538) |
| author2 | Azizan As’arry (19648346) Niu Jing (12052706) Mohd Idris Shah Ismail (22177541) Hafiz Rashidi Ramli (12050666) M. Y. M. Zuhri (22177544) Aidin Delgoshaei (22177547) |
| author2_role | author author author author author author |
| author_facet | Ma Haohao (22177538) Azizan As’arry (19648346) Niu Jing (12052706) Mohd Idris Shah Ismail (22177541) Hafiz Rashidi Ramli (12050666) M. Y. M. Zuhri (22177544) Aidin Delgoshaei (22177547) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ma Haohao (22177538) Azizan As’arry (19648346) Niu Jing (12052706) Mohd Idris Shah Ismail (22177541) Hafiz Rashidi Ramli (12050666) M. Y. M. Zuhri (22177544) Aidin Delgoshaei (22177547) |
| dc.date.none.fl_str_mv | 2025-09-03T17:47:08Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330979.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Partial_polishing_of_experimental_data_/30046106 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Neuroscience Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified rbf neural networks preston &# 8217 finite element analysis experimental results show 0001 &# 181 manual polishing experiment enhance force control achieving high precision robot polishing process robot polishing polishing pressure high efficiency adaptive force xlink "> tool speed surface roughness strong guarantee sandpaper type roughness prediction pd iteration generate trajectories feed speed curved workpieces baseline method average compared |
| dc.title.none.fl_str_mv | Partial polishing of experimental data. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>In this study, an adaptive force-position-speed collaborative process planning framework for robot polishing was proposed to improve the stability of the robot polishing process. The material removal model based on Preston’s theory was studied, and the factors of polishing pressure, tool speed, feed speed, and sandpaper type were considered to design the manual polishing experiment. The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. For curved workpieces, the curvature adaptive interpolation method is introduced to generate trajectories. An adaptive impedance control strategy is implemented to enhance force control, and PD iteration and RBF neural networks are used to ensure stable contact force and accuracy. The experimental results show that the root mean square error (RMSE) accuracy of the established roughness prediction model reaches 0.0001 µm, the proposed force control method is more stable, and the surface roughness is reduced by 20.79% on average compared to the baseline method, which proves the effectiveness of the framework in achieving high precision and high efficiency of robot polishing.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_87f99c330aeca8ea13e864e1c7c623c4 |
| identifier_str_mv | 10.1371/journal.pone.0330979.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30046106 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Partial polishing of experimental data.Ma Haohao (22177538)Azizan As’arry (19648346)Niu Jing (12052706)Mohd Idris Shah Ismail (22177541)Hafiz Rashidi Ramli (12050666)M. Y. M. Zuhri (22177544)Aidin Delgoshaei (22177547)BiophysicsNeuroscienceBiotechnologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedrbf neural networkspreston &# 8217finite element analysisexperimental results show0001 &# 181manual polishing experimentenhance force controlachieving high precisionrobot polishing processrobot polishingpolishing pressurehigh efficiencyadaptive forcexlink ">tool speedsurface roughnessstrong guaranteesandpaper typeroughness predictionpd iterationgenerate trajectoriesfeed speedcurved workpiecesbaseline methodaverage compared<div><p>In this study, an adaptive force-position-speed collaborative process planning framework for robot polishing was proposed to improve the stability of the robot polishing process. The material removal model based on Preston’s theory was studied, and the factors of polishing pressure, tool speed, feed speed, and sandpaper type were considered to design the manual polishing experiment. The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. For curved workpieces, the curvature adaptive interpolation method is introduced to generate trajectories. An adaptive impedance control strategy is implemented to enhance force control, and PD iteration and RBF neural networks are used to ensure stable contact force and accuracy. The experimental results show that the root mean square error (RMSE) accuracy of the established roughness prediction model reaches 0.0001 µm, the proposed force control method is more stable, and the surface roughness is reduced by 20.79% on average compared to the baseline method, which proves the effectiveness of the framework in achieving high precision and high efficiency of robot polishing.</p></div>2025-09-03T17:47:08ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0330979.t004https://figshare.com/articles/dataset/Partial_polishing_of_experimental_data_/30046106CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300461062025-09-03T17:47:08Z |
| spellingShingle | Partial polishing of experimental data. Ma Haohao (22177538) Biophysics Neuroscience Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified rbf neural networks preston &# 8217 finite element analysis experimental results show 0001 &# 181 manual polishing experiment enhance force control achieving high precision robot polishing process robot polishing polishing pressure high efficiency adaptive force xlink "> tool speed surface roughness strong guarantee sandpaper type roughness prediction pd iteration generate trajectories feed speed curved workpieces baseline method average compared |
| status_str | publishedVersion |
| title | Partial polishing of experimental data. |
| title_full | Partial polishing of experimental data. |
| title_fullStr | Partial polishing of experimental data. |
| title_full_unstemmed | Partial polishing of experimental data. |
| title_short | Partial polishing of experimental data. |
| title_sort | Partial polishing of experimental data. |
| topic | Biophysics Neuroscience Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified rbf neural networks preston &# 8217 finite element analysis experimental results show 0001 &# 181 manual polishing experiment enhance force control achieving high precision robot polishing process robot polishing polishing pressure high efficiency adaptive force xlink "> tool speed surface roughness strong guarantee sandpaper type roughness prediction pd iteration generate trajectories feed speed curved workpieces baseline method average compared |