The design of cold forming processes requires the availability of a procedure able to deal with the prevention of ductile fracture. In fact, the ability to predict fracture represents a powerful tool to improve the production quality in mechanical industry. In this paper, artificial intelligence (AI) techniques are applied to ductile fracture prediction in cold forming operations. The main advantage of the application of AI tools and in particular, of artificial neural networks (ANN), is the possibility to obtain a predictive tool with a wide applicability. The prediction results obtained in this paper fully demonstrate the usefulness of the proposed approach.

DI LORENZO R, G INGARAO, F MICARI (2006). On the use of artificial intelligence tools for fracture forecast in cold forming operations. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 177, 315-318 [10.1016/j.jmatprotec.2006.04.032].

On the use of artificial intelligence tools for fracture forecast in cold forming operations

DI LORENZO, Rosa;INGARAO, Giuseppe;MICARI, Fabrizio
2006-01-01

Abstract

The design of cold forming processes requires the availability of a procedure able to deal with the prevention of ductile fracture. In fact, the ability to predict fracture represents a powerful tool to improve the production quality in mechanical industry. In this paper, artificial intelligence (AI) techniques are applied to ductile fracture prediction in cold forming operations. The main advantage of the application of AI tools and in particular, of artificial neural networks (ANN), is the possibility to obtain a predictive tool with a wide applicability. The prediction results obtained in this paper fully demonstrate the usefulness of the proposed approach.
2006
DI LORENZO R, G INGARAO, F MICARI (2006). On the use of artificial intelligence tools for fracture forecast in cold forming operations. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 177, 315-318 [10.1016/j.jmatprotec.2006.04.032].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/12224
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