Indexing and Retrieval in Metal Stamping Die Design Using Case-based Reasoning

2003 ◽  
Vol 3 (4) ◽  
pp. 353-362 ◽  
Author(s):  
S. B. Tor ◽  
G. A. Britton , and ◽  
W. Y. Zhang
Keyword(s):  
Search Space ◽  
Die Design ◽  
Graph Representation ◽  
Case Based Reasoning ◽  
Stamping Die ◽  
Indexing And Retrieval ◽  
Metal Stamping ◽  
High Level ◽  
Relation Graph ◽  
Case Based

This paper presents a case-based reasoning (CBR) methodology for metal stamping die design, that in particular addresses the indexing and retrieval of die design cases. A feature relation graph representation of stamped metal parts are used to create a high level of geometric abstraction, which is used to index design cases quickly and accurately. Though the potential search space for case retrieval is huge, by employing a novel dual-step similarity analysis between a new stamped part and existing parts in the case library, the proposed retrieval strategy can narrow down the search space efficiently and retrieve the most similar case in a reasonable period of time. An illustrative example is included to demonstrate the operation of the proposed approach and show its effectiveness in speeding up stamping die design.

scholarly journals Case-Based Reasoning with an Artificial Neural Network for Decision Support in Situations at Complex Technological Objects of Urban Infrastructure

2021 ◽  
Vol 4 (4) ◽  
pp. 73
Author(s):  
Igor Glukhikh ◽  
Dmitry Glukhikh
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Environmental Influence ◽  
Case Based Reasoning ◽  
Technological Object ◽  
Technological Facility ◽  
High Level ◽  
Case Based

The article considers the tasks of intellectual support for decision support in relation to a complex technological object. The relevance is determined by a high level of responsibility, together with a variety of possible situations at a complex technological facility. The authors consider case-based reasoning (CBR) as a method for decision support. For a complex technological object, the problem defined is the uniqueness of the situations, which is determined by a variety of elements and the possible environmental influence. This problem complicates the implementation of CBR, especially the stages of comparing situations and a further selection of the most similar situation from the database. As a solution to this problem, the authors consider the use of neural networks. The work examines two neural network architectures. The first part of the research presents a neural network model that builds upon the multilayer perceptron. The second part considers the “Comparator-Adder” architecture. Experiments have shown that the proposed neural network architecture “Comparator-Adder” showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise.

An Enhanced Graph Representation and Heuristic Tabu Search Approach for Flexible and Efficient 3D Shape Matching

2014 ◽  
Vol 14 (3) ◽  
Author(s):  
Yuhang Sun ◽  
Liping Chen ◽  
Yunbao Huang ◽  
Sha Wan
Keyword(s):  
Tabu Search ◽  
Shape Matching ◽  
Computation Time ◽  
Search Space ◽  
Boundary Representation ◽  
Graph Representation ◽  
3D Shape ◽  
Core Idea ◽  
High Level ◽  
3D Shape Matching

3D shape matching is widely used in engineering design for model retrieval, shape similarity assessment or collaborative development. In this paper, an enhanced graph representation and heuristic tabu search based approach is presented to enable flexible and efficient 3D shape matching. The core idea includes (1) generic shape features are recognized from boundary representation (B-rep) of 3D shape as many as possible to incorporate domain design knowledge, (2) an enhanced graph representation of 3D shape is constructed by mixing faces of B-rep and recognized features, to achieve meaningful matching results at low-level of faces or high-level of features satisfying various design intents, and (3) a tabu list of possible improper matches is built to reduce the search space so that the optimal result can be efficiently obtained. Finally, Two examples are demonstrated to show that both two levels of 3D shape matching results can be efficiently obtained for various design intents in the engineering applications, only not more than 18% computation time is required when compared with a typical shape matching method, and it takes only 20 s when the number of matching nodes is more than 460.

Adaptive Aluminum Extrusion Die Design Using Case-Based Reasoning and Artificial Neural Networks

2011 ◽  
Vol 383-390 ◽  
pp. 6747-6754
Author(s):  
Suthep Butdee
Keyword(s):  
Maximum Yield ◽  
Adaptive Method ◽  
Die Design ◽  
Case Based Reasoning ◽  
Aluminum Extrusion ◽  
Optimum Parameters ◽  
Extrusion Die ◽  
Extrusion Die Design ◽  
Aluminum Profile Extrusion ◽  
Case Based

Aluminum extrusion die design involves with two critical parts; die features and its parameters. Presently, die design process is performed by adaptation approach. The previous dies together with their parameters are collected and stored in a database under the well-memory organization. Case-Based Reasoning (CBR) has been applied and enhanced the design productivity. However, the CBR method has an excellent ability only that an exact or similar design features are existed. Reality, aluminum die design requires regularly changed according to the profile changes. Therefore, it needs to predict optimum parameters to assist in the process of aluminum profile extrusion. This paper presents the redesign process using adaptive method. In this case, CBR & ANN method are combined and development. The CBR uses for die feature adaptation; whereas the ANN is used for parameter adaptation and prediction to a new profile and die design. The actual production yield is given and the ANN will find the best size of billet length in order to receive the maximum yield.

Some aspects of a knowledge-based approach for automating progressive metal stamping die design

1994 ◽  
Vol 24 (1) ◽  
pp. 81-96 ◽  
Author(s):  
B.T. Cheok ◽  
K.Y. Foong ◽  
A.Y.C. Nee ◽  
C.H. Teng
Keyword(s):  
Die Design ◽  
Knowledge Based ◽  
Stamping Die ◽  
Metal Stamping
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