标题:An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining
作者:Xu L.; Huang C.; Li C.; Wang J.; Liu H.; Wang X.
作者机构:[Xu, L] Center for Advanced Jet Engineering Technologies (CaJET), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture (Ministry of Educ 更多
通讯作者:Huang, C(chuanzhenh@sdu.edu.cn)
通讯作者地址:[Huang, C] Center for Advanced Jet Engineering Technologies (CaJET), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture (Ministry of E 更多
来源:Journal of Intelligent Manufacturing
出版年:2020
DOI:10.1007/s10845-020-01573-2
关键词:ANN model; GPR model; ICBR method; VPSO algorithm
摘要:In the high speed milling process, the accurate predictions of surface roughness and residual stress can avoid the deterioration of machined surface quality. But it’s hard to estimate the surface roughness and residual stress under different tool wear status and cutting parameters. In this work, a novel intelligent reasoning method-improved case based reasoning (ICBR) was proposed to predict the surface roughness and residual stress. The inputs of ICBR are cutting parameters and tool wear status. The corresponding outputs of ICBR are surface roughness and residual stress. In the ICBR, K-nearest neighbor method and artificial neural network (ANN) as case retrieval was introduced to retrieve the K similar cases to the inputs. Through retrieving K similar cases, the Gaussian process regression (GPR) model as case reuse was established to output the surface roughness and residual stress. The vibration particle swarm optimization algorithm is proposed to optimize the ANN and GPR models. The high speed milling experiments of Compacted Graphite Iron was performed to validate the performance of ICBR. The experimental results showed that the cutting speed is the most important factor affecting the surface roughness. The feed rate is the most important factor affecting the residual stress. The ICBR gives the accurate estimation of surface roughness with the Mean Absolute Percentage Error of 11.6%. As for residual stress, the prediction accuracy using ICBR is 87.5%. Compared with Back-Propagation neural network, standard CBR and GPR models, the ICBR has better predictive performance and can be used for estimations of surface roughness and residual stress in the actual machining process. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
收录类别:SCOPUS
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083589603&doi=10.1007%2fs10845-020-01573-2&partnerID=40&md5=72dd6e15e1de046c1fb7e4f5ec89e59b
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