Reliability assessment of CNC machining center based on Weibull neural network
type: Item
instance of: scholarly article
title: Reliability assessment of CNC machining center based on Weibull neural network
zbMATH Open document ID: 1394.90242
DOI: 10.1155/2015/292197
author: Zhaojun Yang
author: Chao Chen
author: Jili Wang
author: Guofa Li
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: CNC machining centers, as the key device in modern manufacturing industry, are complicated electrohydraulic products. The reliability is the most important index of CNC machining centers. However, simple life distributions hardly reflect the true law of complex system reliability with many kinds of failure mechanisms. Due to Weibull model's versatility and relative simplicity and artificial neural networks' (ANNs) high capability of approximating, they are widely used in reliability engineering and elsewhere. Considering the advantages of these two models, this paper defined a novel model: Weibull neural network (WNN). WNN inherits the hierarchical structure from ANNs which include three layers, namely, input layer, hidden layer, and output layer. Based on more than 3000\ h field test data of CNC machining centers, WNN has been successfully applied in comprehensive operation data analysis. The results show that WNN has good approximation ability and generalization performance in reliability assessment of CNC machining centers.
Mathematics Subject Classification ID: 90B25
Mathematics Subject Classification ID: 62N05
Mathematics Subject Classification ID: 62-07
zbMATH DE Number: 6925980
Wikidata QID: Q59117890
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/292197
OpenAlex ID: W2174350338
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description: scientific article; zbMATH DE number 6925980