A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS
Rail grinding profile prediction in different grinding patterns is important to improve the grinding quality for the rail grinding operation site. However, because of high-dimensional and strong nonlinearity between grinding amount and grinding parameters, the prediction error and computational cost...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2020-07-01
|
| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814020938493 |
| _version_ | 1828813651013795840 |
|---|---|
| author | Huan Xie Xiang Chen Wei Zeng Wensheng Qiu Tao Ren |
| author_facet | Huan Xie Xiang Chen Wei Zeng Wensheng Qiu Tao Ren |
| author_sort | Huan Xie |
| collection | DOAJ |
| description | Rail grinding profile prediction in different grinding patterns is important to improve the grinding quality for the rail grinding operation site. However, because of high-dimensional and strong nonlinearity between grinding amount and grinding parameters, the prediction error and computational cost is relatively high. As a result, the accuracy and efficiency of conventional methods cannot be guaranteed. In this article, an accurate and efficient rail grinding profile prediction method is proposed, in which an interval segmentation approach is proposed to improve the prediction efficiency based on the geometric characteristic of a rail profile. Then, the accurate area integral approach with cubic NURBS is used as the grinding area calculation approach to improve the prediction accuracy. Finally, the normal length index is introduced to evaluate the prediction accuracy. The accuracy and stability of the proposed method are verified by comparing a conventional approach based on a practical experiment. The results demonstrate that the proposed method can predict the rail grinding profile in any grinding pattern with high accuracy and efficiency. Meanwhile, its prediction stability basically agrees with the conventional approach. |
| first_indexed | 2024-12-12T10:05:49Z |
| format | Article |
| id | doaj.art-b056a59390d34e04b0871434d4de2ead |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-12T10:05:49Z |
| publishDate | 2020-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-b056a59390d34e04b0871434d4de2ead2022-12-22T00:27:54ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402020-07-011210.1177/1687814020938493A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBSHuan Xie0Xiang Chen1Wei Zeng2Wensheng Qiu3Tao Ren4School of Mechanical Engineering, Xijing University, Xi’an, ChinaSchool of Mechanical Engineering, Xijing University, Xi’an, ChinaSchool of Mechanical Engineering, Xi’an Shiyou University, Xi’an, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha, ChinaSchool of Mechanical Engineering, Xi’an Shiyou University, Xi’an, ChinaRail grinding profile prediction in different grinding patterns is important to improve the grinding quality for the rail grinding operation site. However, because of high-dimensional and strong nonlinearity between grinding amount and grinding parameters, the prediction error and computational cost is relatively high. As a result, the accuracy and efficiency of conventional methods cannot be guaranteed. In this article, an accurate and efficient rail grinding profile prediction method is proposed, in which an interval segmentation approach is proposed to improve the prediction efficiency based on the geometric characteristic of a rail profile. Then, the accurate area integral approach with cubic NURBS is used as the grinding area calculation approach to improve the prediction accuracy. Finally, the normal length index is introduced to evaluate the prediction accuracy. The accuracy and stability of the proposed method are verified by comparing a conventional approach based on a practical experiment. The results demonstrate that the proposed method can predict the rail grinding profile in any grinding pattern with high accuracy and efficiency. Meanwhile, its prediction stability basically agrees with the conventional approach.https://doi.org/10.1177/1687814020938493 |
| spellingShingle | Huan Xie Xiang Chen Wei Zeng Wensheng Qiu Tao Ren A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS Advances in Mechanical Engineering |
| title | A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS |
| title_full | A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS |
| title_fullStr | A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS |
| title_full_unstemmed | A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS |
| title_short | A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS |
| title_sort | novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic nurbs |
| url | https://doi.org/10.1177/1687814020938493 |
| work_keys_str_mv | AT huanxie anovelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT xiangchen anovelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT weizeng anovelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT wenshengqiu anovelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT taoren anovelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT huanxie novelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT xiangchen novelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT weizeng novelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT wenshengqiu novelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs AT taoren novelpredictionmethodforrailgrindingprofilebasedonanintervalsegmentationapproachandaccurateareaintegralwithcubicnurbs |