Part Recognition with Neural Network Implemention
碩士 === 中原大學 === 工業工程研究所 === 81 === The objective of this research is to develop a three- dimensional (3D) part recognition method by implementing neural network. The recognition procedures developed in this research has two phases. The first phas...
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ndltd-TW-081CYCU00300062015-10-13T17:44:41Z http://ndltd.ncl.edu.tw/handle/60265695130418131986 Part Recognition with Neural Network Implemention 應用類神經網路進行工件辨識 Lee,Shung Chung 李淑瓊 碩士 中原大學 工業工程研究所 81 The objective of this research is to develop a three- dimensional (3D) part recognition method by implementing neural network. The recognition procedures developed in this research has two phases. The first phase is trying to extract the data structure from Computer Aided Design (CAD) system and convert it into syntatic patterns which are then feed to a neural network system to perform two-dimensional part feature recognition. In order to recognize three- dimensional compound feature of parts, the major tasks in second phase consist of developing some feature recognition rules and combining the results from phase one to develop an adjacent matrix for each two dimensional pattern. The adjacent matrix is the input into 3D neural network recognizer to recognize the 3D compound part features. Basically, the features can be recognized in this research are limited on steps and slots which include standard slot, i-directional blind slot, blind slot, standard step, tri- directional blind step, and blind step. In addition, an experiment was conducted in this research to study the relationship between system performance and neural network parameters such as batch learning sample, learning rate, and number of hidden layer. Su,Chwen Tzeng; Jiang,Ruey Ching 蘇純繒;江瑞清 1993 學位論文 ; thesis 122 zh-TW |
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碩士 === 中原大學 === 工業工程研究所 === 81 === The objective of this research is to develop a three-
dimensional (3D) part recognition method by implementing
neural network. The recognition procedures developed in this
research has two phases. The first phase is trying to
extract the data structure from Computer Aided Design (CAD)
system and convert it into syntatic patterns which are then
feed to a neural network system to perform two-dimensional
part feature recognition. In order to recognize three-
dimensional compound feature of parts, the major tasks in
second phase consist of developing some feature
recognition rules and combining the results from phase one
to develop an adjacent matrix for each two dimensional
pattern. The adjacent matrix is the input into 3D neural
network recognizer to recognize the 3D compound part features.
Basically, the features can be recognized in this research
are limited on steps and slots which include standard
slot, i-directional blind slot, blind slot, standard step, tri-
directional blind step, and blind step. In addition,
an experiment was conducted in this research to study
the relationship between system performance and neural
network parameters such as batch learning sample, learning
rate, and number of hidden layer.
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author2 |
Su,Chwen Tzeng; Jiang,Ruey Ching |
author_facet |
Su,Chwen Tzeng; Jiang,Ruey Ching Lee,Shung Chung 李淑瓊 |
author |
Lee,Shung Chung 李淑瓊 |
spellingShingle |
Lee,Shung Chung 李淑瓊 Part Recognition with Neural Network Implemention |
author_sort |
Lee,Shung Chung |
title |
Part Recognition with Neural Network Implemention |
title_short |
Part Recognition with Neural Network Implemention |
title_full |
Part Recognition with Neural Network Implemention |
title_fullStr |
Part Recognition with Neural Network Implemention |
title_full_unstemmed |
Part Recognition with Neural Network Implemention |
title_sort |
part recognition with neural network implemention |
publishDate |
1993 |
url |
http://ndltd.ncl.edu.tw/handle/60265695130418131986 |
work_keys_str_mv |
AT leeshungchung partrecognitionwithneuralnetworkimplemention AT lǐshūqióng partrecognitionwithneuralnetworkimplemention AT leeshungchung yīngyònglèishénjīngwǎnglùjìnxínggōngjiànbiànshí AT lǐshūqióng yīngyònglèishénjīngwǎnglùjìnxínggōngjiànbiànshí |
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