Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
384117 | Expert Systems with Applications | 2012 | 8 Pages |
Abstract
The objective of this paper is to develop and test a model of manufacturing cost estimating of piping elements during the early design phase through the application of artificial neural networks (ANN). The developed model can help designers to make decisions at the early phases of the design process. An ANN model would allow obtaining a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. The developed model is compared with traditional neural networks and conventional regression models. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of shell and tube heat exchangers.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Orlando Duran, Juan Maciel, Nibaldo Rodriguez,