Artificial neural networks for the cost estimation of stamping dies


Ozcan B., FIĞLALI A.

NEURAL COMPUTING & APPLICATIONS, vol.25, pp.717-726, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25
  • Publication Date: 2014
  • Doi Number: 10.1007/s00521-014-1546-8
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.717-726
  • Keywords: Artificial neural network, Cost estimation, Regression analysis, Stamping die, REGRESSION-ANALYSIS, DESIGN, PREDICTION, PRODUCT, UNCERTAINTY, PARTS, MODEL
  • Kocaeli University Affiliated: Yes

Abstract

Although artificial neural networks (ANN) are more known in the field of image recognition and forecasting, cost estimation has become another emerging area in recent years. In this study, the establishment of an intelligent system was attempted for forecasting the total cost of sheet metal stamping dies. In this context, where the cost of stamping dies is estimated with a conventional approach which has been applied in the company up to now, the ANN and multiple regression analysis and the performance of the three cost-estimation models are examined. The examinations are based on the data of previous costs and use a number of critical criteria which are decided by experienced tool makers and engineers from every level of the organization of the seven companies which produce stamping dies. The comparative study reveals that the ANN system outperforms the traditional linear regression analysis model and conventional approach used for cost estimation. Thus, it is possible for firms which produce stamping dies to obtain a fairly accurate prediction with an ANN model and determined criteria.