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1. |
Record Nr. |
UNINA990001777840403321 |
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Autore |
Hayat, M. Arif |
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Titolo |
Stains and cytochemical methods / M.A. Hayat |
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Pubbl/distr/stampa |
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New York, : Plenum Press, 1993 |
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ISBN |
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Descrizione fisica |
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XVII, 455 p. : ill. ; 23 cm |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910637782503321 |
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Autore |
Prates Pedro |
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Titolo |
Recent Advances and Applications of Machine Learning in Metal Forming Processes |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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ISBN |
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Descrizione fisica |
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1 electronic resource (210 p.) |
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Soggetti |
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Technology: general issues |
History of engineering & technology |
Mining technology & engineering |
History of engineering and technology |
Mining technology and engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics. |
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