Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering
Combining fuzzy neural network and laser surface data measurement, a novel model reconstruction methodology is presented. This model reconstruction scheme includes two main parts, one is surface data measurement system, and the other one is model reconstruction algorithm. The surface data me...
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2004
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my-inti-eprints.3202016-06-16T07:35:12Z http://eprints.intimal.edu.my/320/ Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering Nagajyothi, D. QA76 Computer software Combining fuzzy neural network and laser surface data measurement, a novel model reconstruction methodology is presented. This model reconstruction scheme includes two main parts, one is surface data measurement system, and the other one is model reconstruction algorithm. The surface data measurement system consists of a vision system with a smart laser camera and a PC computer. The system is developed to measure data for freeform surface with complex shape. Using an Adaptive Network based Fuzzy Inference System (ANFIS), the model reconstruction algorithm is designed. For demonstrating the effectiveness of the presented scheme, a group points cloud data with good accuracy. This is measured by the presented data measurement system for an existing part and is taken as data sample for training the ANFIS. The trained ANFIS is taken as surface data model. By comparing the surface data, which is from trained ANFIS, with the data sample value, it can be found that the ANFIS model can match the real surface very well. World Scientific Publishing 2004 Book Section PeerReviewed text en http://eprints.intimal.edu.my/320/1/1.pdf Nagajyothi, D. (2004) Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering. In: People, Knowledge and Technology: What Have We Learnt So Far? World Scientific Publishing, p. 397. ISBN 978-981-4481-00-7 10.1142/9789812702081_0061 |
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QA76 Computer software Nagajyothi, D. Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
description |
Combining fuzzy neural network and
laser surface data measurement, a novel
model reconstruction methodology is
presented. This model reconstruction
scheme includes two main parts, one is
surface data measurement system, and
the other one is model reconstruction
algorithm. The surface data
measurement system consists of a vision
system with a smart laser camera and a PC computer. The system is developed
to measure data for freeform surface
with complex shape. Using an Adaptive
Network based Fuzzy Inference System
(ANFIS), the model reconstruction
algorithm is designed. For demonstrating
the effectiveness of the presented
scheme, a group points cloud data with
good accuracy. This is measured by the
presented data measurement system for
an existing part and is taken as data
sample for training the ANFIS. The
trained ANFIS is taken as surface data
model. By comparing the surface data,
which is from trained ANFIS, with the
data sample value, it can be found that
the ANFIS model can match the real
surface very well. |
format |
Book Section |
author |
Nagajyothi, D. |
author_facet |
Nagajyothi, D. |
author_sort |
Nagajyothi, D. |
title |
Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
title_short |
Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
title_full |
Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
title_fullStr |
Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
title_full_unstemmed |
Application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
title_sort |
application of adaptive network based fuzzy inference system for model reconstruction in reverse engineering |
publisher |
World Scientific Publishing |
publishDate |
2004 |
url |
http://eprints.intimal.edu.my/320/1/1.pdf http://eprints.intimal.edu.my/320/ |
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1644541177938051072 |
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13.250246 |