Please use this identifier to cite or link to this item:
|Title:||A neural network approach to determining optimal inspection sampling size for CMM|
|Authors:||Zhang, Y.F. |
|Keywords:||Back-propagation neural networks|
|Source:||Zhang, Y.F.,Nee, A.Y.C.,Fuh, J.Y.H.,Neo, K.S.,Loy, H.K. (1996). A neural network approach to determining optimal inspection sampling size for CMM. Computer Integrated Manufacturing Systems 9 (3) : 161-169. ScholarBank@NUS Repository.|
|Abstract:||This paper reports a neural network approach to determining the optimal inspection sampling size of 'hole' features using the Coordinate Measuring Machine (CMM). Factors which could affect sample size due to design, manufacturing, and measurement related factors, i.e. size, dimensional and geometrical tolerances, machining processes, and confidence levels, have been studied. Machining process type, size, and tolerance band have been identified as the known factors which may affect the sample size required. Experiments have been carried out to collect sampling size data versus the variation of these factors for different 'hole' features. The implicit correlation between the sample size and these factors, has been achieved by training a back-propagation neural network using the collected data. The neural network architecture is described, and the test of the trained neural network on a few new 'hole' features is presented to highlight the applicability of this approach. Copyright © 1996 Elsevier Science Ltd.|
|Source Title:||Computer Integrated Manufacturing Systems|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 15, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.