Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding
This paper aims to accurately predict the surface quality of the special-shaped stone by robotic grinding and effectively guide the adjustment of process parameters to ensure stable grinding quality, applies a support vector machine model based on improved whale optimization algorithm (IWOA-SVR), so...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9786766/ |
| _version_ | 1818236920174477312 |
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| author | Fangchen Yin Qingzhi Ji Changcai Cun |
| author_facet | Fangchen Yin Qingzhi Ji Changcai Cun |
| author_sort | Fangchen Yin |
| collection | DOAJ |
| description | This paper aims to accurately predict the surface quality of the special-shaped stone by robotic grinding and effectively guide the adjustment of process parameters to ensure stable grinding quality, applies a support vector machine model based on improved whale optimization algorithm (IWOA-SVR), so as to establish a prediction model of surface roughness of special-shaped stone and a selection method of process parameters. The proposed IWOA-SVR was used to improve the prediction accuracy of support vector machine regression model, and a prediction model of surface roughness (<inline-formula> <tex-math notation="LaTeX">$R_{a})$ </tex-math></inline-formula> for stone was established. On this basis, the relationship between the output parameters (surface roughness) and inutput parameters (spindle speed, feed speed, cutting depth and cutting width)was explored to obtain more suitable process parameters. Combining the grinding experiment data of special-shaped stone, the comparison was carried out between IWOA-SVR and the SVR model optimized by the commonly used optimization algorithms (grid-search optimization algorithm (GS) and whale optimization algorithm (WOA)). Under the same sample condition, the prediction error of GS-SVR is the most large, and the average prediction error of IWOA-SVR is only 86.1% if that of WOA-SVR, the training time is shortened by 54.4%. The influence of process parameters on surface roughness obtained by IWOA-SVR can effectively guide the selection and adjustment of process parameters. It has good guiding significance for maintaining the excellent grinding quality of special-shaped stone. |
| first_indexed | 2024-12-12T12:17:31Z |
| format | Article |
| id | doaj.art-6ac09e9fdcfa462599ffd8fb4904db83 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T12:17:31Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6ac09e9fdcfa462599ffd8fb4904db832022-12-22T00:24:44ZengIEEEIEEE Access2169-35362022-01-0110676156762910.1109/ACCESS.2022.31798189786766Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic GrindingFangchen Yin0https://orcid.org/0000-0003-1579-9954Qingzhi Ji1https://orcid.org/0000-0001-8810-1970Changcai Cun2https://orcid.org/0000-0002-8023-7934National and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products, Huaqiao University, Xiamen, ChinaNational and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products, Huaqiao University, Xiamen, ChinaNational and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products, Huaqiao University, Xiamen, ChinaThis paper aims to accurately predict the surface quality of the special-shaped stone by robotic grinding and effectively guide the adjustment of process parameters to ensure stable grinding quality, applies a support vector machine model based on improved whale optimization algorithm (IWOA-SVR), so as to establish a prediction model of surface roughness of special-shaped stone and a selection method of process parameters. The proposed IWOA-SVR was used to improve the prediction accuracy of support vector machine regression model, and a prediction model of surface roughness (<inline-formula> <tex-math notation="LaTeX">$R_{a})$ </tex-math></inline-formula> for stone was established. On this basis, the relationship between the output parameters (surface roughness) and inutput parameters (spindle speed, feed speed, cutting depth and cutting width)was explored to obtain more suitable process parameters. Combining the grinding experiment data of special-shaped stone, the comparison was carried out between IWOA-SVR and the SVR model optimized by the commonly used optimization algorithms (grid-search optimization algorithm (GS) and whale optimization algorithm (WOA)). Under the same sample condition, the prediction error of GS-SVR is the most large, and the average prediction error of IWOA-SVR is only 86.1% if that of WOA-SVR, the training time is shortened by 54.4%. The influence of process parameters on surface roughness obtained by IWOA-SVR can effectively guide the selection and adjustment of process parameters. It has good guiding significance for maintaining the excellent grinding quality of special-shaped stone.https://ieeexplore.ieee.org/document/9786766/Special-shaped natural stonegrinding complexitysurface roughness |
| spellingShingle | Fangchen Yin Qingzhi Ji Changcai Cun Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding IEEE Access Special-shaped natural stone grinding complexity surface roughness |
| title | Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding |
| title_full | Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding |
| title_fullStr | Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding |
| title_full_unstemmed | Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding |
| title_short | Data-Driven Modeling and Prediction Analysis for Surface Roughness of Special-Shaped Stone by Robotic Grinding |
| title_sort | data driven modeling and prediction analysis for surface roughness of special shaped stone by robotic grinding |
| topic | Special-shaped natural stone grinding complexity surface roughness |
| url | https://ieeexplore.ieee.org/document/9786766/ |
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