Please use this identifier to cite or link to this item:
|Title:||A neural network approach for early cost estimation of packaging products||Authors:||Zhang, Y.F.
|Issue Date:||1-Apr-1998||Citation:||Zhang, Y.F.,Fuh, J.Y.H. (1998-04-01). A neural network approach for early cost estimation of packaging products. Computers and Industrial Engineering 34 (2-4) : 433-450. ScholarBank@NUS Repository.||Abstract:||Product costs need to be identified early, i.e., during the design stage, where they can be controlled best. This implies the need to estimate the product's cost without full knowledge of the manufacturing process plans. In this paper, a feature-based cost estimation using a back-propagation neural network is proposed and a prototype system has been developed for estimating the costs of packaging products based on design information only. All the cost-related features of a product design were extracted and quantified according to their cost effects. The correlation between these cost-related features and the final cost of the product was established by training a back-propagation neural network using historical cost data. The extraction of cost-related features and the construction, training and validation of the neural network are described. The performance of the trained neural network based on a set of testing samples is also given. © 1998 Elsevier Science Ltd. All rights reserved.||Source Title:||Computers and Industrial Engineering||URI:||http://scholarbank.nus.edu.sg/handle/10635/54476||ISSN:||03608352|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 9, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.