Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study

Increasing competition in sheet metal operations has urged those companies to search for tools that generate accurate cost estimates within a short time period. The requirement for on-line generation implies that the underlying cost estimate needs to be generated without extensive process planning first. Analysis has been conducted on developing a less-detailed method, based on a brief analysis of the CAD-file. Cost formulas are composed by applying regression techniques and neural networks. A case study is used to compare both methods. The results obtained indicate that neural networks give better results but are still mainly considered black boxes. (c) 2007 Elsevier B.V. All rights reserved.