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Research on intelligent generation method of process dimension based on feature constraint.

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Title: Research on intelligent generation method of process dimension based on feature constraint.
Authors: Zhou, Honggen1,2 (AUTHOR), Sheng, Sushan1 (AUTHOR), Liu, Jinfeng1,2 (AUTHOR) liujinfeng0816@163.com, Zhao, Peng1 (AUTHOR), Dong, Jianwei1 (AUTHOR), Cao, Xuwu1 (AUTHOR), Liu, Xiaojun3 (AUTHOR)
Source: International Journal of Advanced Manufacturing Technology. Sep2021, Vol. 116 Issue 3/4, p1003-1021. 19p. 1 Color Photograph, 1 Illustration, 13 Diagrams, 2 Charts.
Subjects: Computer-aided process planning, Computer-aided design, Product design
Abstract: Process dimensioning, as an important component of 3D process design, is the key to realize the high integration of computer-aided process planning(CAPP)and computer-aided design (CAD) in intelligent manufacturing. At present, the product design dimension cannot express the process requirements, and the dimensioning method cannot meet the requirements of the rapid creation of the process model. For this reason, an intelligent generation method of process dimension for 3D process design is proposed. Firstly, the process dimension is divided into two types: the shaping dimension and location dimension. The shaping dimension is created based on removal volume, and the location dimension is generated based on using the constraints of features. Secondly, the association method of "feature-dimension-removal volume" model is created by analyzing the correlation between the feature, dimension, and the removal volume. Then, the constraint relationship between dimension and dimension priority of the process dimension is established to construct intelligent inspection method for process dimension. Finally, a plate part is used as verification object to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology 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.)
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  Data: Research on intelligent generation method of process dimension based on feature constraint.
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  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Honggen%22">Zhou, Honggen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sheng%2C+Sushan%22">Sheng, Sushan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jinfeng%22">Liu, Jinfeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liujinfeng0816@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Peng%22">Zhao, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Jianwei%22">Dong, Jianwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Xuwu%22">Cao, Xuwu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaojun%22">Liu, Xiaojun</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Sep2021, Vol. 116 Issue 3/4, p1003-1021. 19p. 1 Color Photograph, 1 Illustration, 13 Diagrams, 2 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Computer-aided+process+planning%22">Computer-aided process planning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+design%22">Computer-aided design</searchLink><br /><searchLink fieldCode="DE" term="%22Product+design%22">Product design</searchLink>
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  Label: Abstract
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  Data: Process dimensioning, as an important component of 3D process design, is the key to realize the high integration of computer-aided process planning(CAPP)and computer-aided design (CAD) in intelligent manufacturing. At present, the product design dimension cannot express the process requirements, and the dimensioning method cannot meet the requirements of the rapid creation of the process model. For this reason, an intelligent generation method of process dimension for 3D process design is proposed. Firstly, the process dimension is divided into two types: the shaping dimension and location dimension. The shaping dimension is created based on removal volume, and the location dimension is generated based on using the constraints of features. Secondly, the association method of "feature-dimension-removal volume" model is created by analyzing the correlation between the feature, dimension, and the removal volume. Then, the constraint relationship between dimension and dimension priority of the process dimension is established to construct intelligent inspection method for process dimension. Finally, a plate part is used as verification object to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology 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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        Value: 10.1007/s00170-021-07045-y
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        Text: English
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              M: 09
              Text: Sep2021
              Type: published
              Y: 2021
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