Paper Info
Title
A neural network based methodology for machining operations selection in Computer-Aided Process Planning for rotationally symmetrical parts
Abstract
The relevant literature on machining operations selection in Computer-Aided Process Planning (CAPP) by decision trees, expert systems and neural networks has been reviewed, highlighting their contributions and shortcomings. This paper aims at contributing to the applicability of back-propagation neural network method for the selection of all possible operations for machining rotationally symmetrical components, by prestructuring the neural network with prior domain knowledge in the form of heuristic or thumb rules. It has been achieved by developing two forms of representation for the input data to the neural network. The external representation is used to enter the crisp values of the input decision variables (namely the feature type and its attributes such as diameter or width, tolerance and surface finish). The purpose of internal representation is to categorize the above crisp values into sets, which correspond to all the possible different ranges of the above input variables encountered in the antecedent ‘IF’ part of the thumb rules mentioned above. The input layer of the neural network has been designed in such a way that one neuronal node is allocated for each of the feature types and the sets of various feature attributes. In the output layer of the neural network, one neuronal node is allocated to each of the various feasible machining operation sequences found in the consequent ‘THEN’ part of the thumb rules. A systematic method for training of the neural network has been presented with the above thumb rules used to serve as guidelines for choosing the input patterns of the training examples. This method simplifies the process of training, reduces the time for preparation of training examples and hence the time to develop the overall process planning system. It can further help ensure that the entire problem domain is represented in a better manner and improve the quality of response of the neural network. The example of an industrially-relevant rotationally symmetrical workpiece has been analyzed using the proposed approach to demonstrate its potential for use in the real manufacturing environment. By this novel methodology, workpieces of complex shapes can be handled by investing a very limited amount of time, hence making it attractive and cost effective for industrial applications.
Year
DOI
Venue
2006
10.1007/s10845-006-0026-0
J. Intelligent Manufacturing
Keywords
Field
DocType
computer-aided process planning · machining process selection · alternative operation sequences · rotational parts · artificial neural networks,decision tree,artificial neural network,expert system,cost effectiveness,domain knowledge,neural network
Computer-aided process planning,Decision tree,Heuristic,Problem domain,Stochastic neural network,Expert system,Time delay neural network,Artificial intelligence,Engineering,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
17
5
1572-8145
Citations 
PageRank 
References 
11
0.75
3
Authors
3
Name
Order
Citations
PageRank
Sankha Deb1121.44
Kalyan Ghosh2292.34
S. Paul3121.52