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Artificial neural networks for the cost estimation of stamping dies

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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.

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Acknowledgments

The authors would like to thank the referees for their insightful and constructive comments and suggestions which have much improved the paper and Atakan Alkan for his computational support in the artificial neural network.

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Authors and Affiliations

  1. Industrial Engineering Department, Faculty of Engineering, Kocaeli University, Umuttepe, 41380, Kocaeli, Turkey

    Burcu Özcan & Alpaslan Fığlalı

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  1. Burcu Özcan
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  2. Alpaslan Fığlalı
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Correspondence to Burcu Özcan.

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Özcan, B., Fığlalı, A. Artificial neural networks for the cost estimation of stamping dies. Neural Comput & Applic 25, 717–726 (2014). https://doi.org/10.1007/s00521-014-1546-8

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