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- W2004633946 abstract "Purpose – Traditional machining centre selection methods may not guarantee a cost effective solution. Properly trained back‐propagation artificial neural network (BPANN) tend to select reasonable machining centres when presented with machining parameters that they have never seen before. The aim of this paper is to demonstrate the applicability of artificial neural networks (ANNs) to machine centre selection problems.Design/methodology/approach – A three‐layer feedforward back‐propagation supervised training approach is selected to address the machining centre selection problem and demonstrated its potential through an example. This is intended to help readers understand implications on manufacturing system design and future research.Findings – Very limited studies attempted the machining centre selection problem. Feedforward ANN approach has been applied to a wide variety of manufacturing problems. Neural networks have training capability to solve problems that are difficult for conventional computers or..." @default.
- W2004633946 created "2016-06-24" @default.
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- W2004633946 date "2007-03-20" @default.
- W2004633946 modified "2023-09-26" @default.
- W2004633946 title "Back‐propagation artificial neural network approach for machining centre selection" @default.
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- W2004633946 doi "https://doi.org/10.1108/17410380710730648" @default.
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