scholarly journals AUTOMATED PART DECOMPOSITION FOR PRODUCT ARCHITECTURE MODELING

2020 ◽  
Vol 1 ◽  
pp. 2365-2374
Author(s):  
J. Redeker ◽  
P. Gebhardt ◽  
A.-K. Reichler ◽  
E. Türck ◽  
K. Dröder ◽  
...  

AbstractThis paper presents an algorithm that contributes to an automatic decomposition of a mechanical part based on geometric features and methods of unsupervised machine learning. For the development of the algorithm, existing techniques of 3D shape segmentation, especially surface-based part segmentation procedures are reviewed and important areas of activities are revealed. The developed multi-step approach results in an abstract product model. This representation leads to a new way of designing and redesigning parts for the novel hybrid manufacturing concept Incremental Manufacturing (IM).

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
József Kuti ◽  
Péter Galambos

This paper introduces the novel concept of Affine Tensor Product (TP) Model and the corresponding model transformation algorithm. Affine TP Model is a unique representation of Linear Parameter Varying systems with advantageous properties that makes it very effective in convex optimization-based controller synthesis. The proposed model form describes the affine geometric structure of the parameter dependencies by a nearly minimum model size and enables a systematic way of geometric complexity reduction. The proposed method is capable of exact analytical model reconstruction and also supports the sampling-based numerical approach with arbitrary discretization grid and interpolation methods. The representation conforms with the latest polytopic model generation and manipulation algorithms. Along these advances, the paper reorganizes and extends the mathematical theory of TP Model Transformation. The practical merit of the proposed concept is demonstrated through a numerical example.


2015 ◽  
Vol 713-715 ◽  
pp. 1570-1573
Author(s):  
Rong Fen Gong ◽  
Mao Xiang Chu ◽  
Yong Hui Yang

An extraction method based on invariance geometric feature is proposed in this paper. This method extracts two types of feature from the object in an image. One type is five invariance statistical features of edge distance. The other is two invariance shape features: rectangular similarity feature and circular similarity feature. Moreover, this proposed method is used to extract defect features for steel plate surface. Its performance is tested in scale and rotation invariance and defects classification. Experimental results show that the novel geometric features have the ability of invariance and can improve the accuracy of classification.


2015 ◽  
Vol 24 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Poul Martin Ravn ◽  
Tómas Vignir Gudlaugsson ◽  
Niels Henrik Mortensen

2010 ◽  
Vol 34 (8) ◽  
pp. S33-S33
Author(s):  
Wenchao Ou ◽  
Haifeng Chen ◽  
Yun Zhong ◽  
Benrong Liu ◽  
Keji Chen

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