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The International Journal of Advanced Manufacturing Technology

Erschienen in:

26.06.2021 | ORIGINAL ARTICLE

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

verfasst von: Honggen Zhou, Sushan Sheng, Jinfeng Liu, Peng Zhao, Jianwei Dong, Xuwu Cao, Xiaojun Liu

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 3-4/2021

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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.

Vorheriger Artikel Study on process and parameter optimisation of selective laser sintering of thermoplastic polyurethane/carbon nanotube powder

Nächster Artikel Single-point incremental forming of AA6061 aluminum alloy at elevated temperatures

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Titel: Research on intelligent generation method of process dimension based on feature constraint
verfasst von: Honggen Zhou
Sushan Sheng
Jinfeng Liu
Peng Zhao
Jianwei Dong
Xuwu Cao
Xiaojun Liu
Publikationsdatum: 26.06.2021
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 3-4/2021
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-07045-y

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