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Modeling and Predicting Surface Roughness in Hard Turning Using a Bayesian Inference-Based HMM-SVM Model
He K.1; Xu Q.2; Jia M.3
2015
Source PublicationIEEE Transactions on Automation Science and Engineering
ISSN15455955
Volume12Issue:3Pages:1092
Abstract

This study proposes a hybrid model for evaluating surface roughness in hard turning using a Bayesian inference-based hidden Markov model and least squares support vector machine (HMM-SVM). The model inputs are multidirectional fusion features that are extracted from the acquired monitoring signals through independent component analysis and singular spectrum analysis. Based on a detailed analysis of the workpiece surface formation mechanism, the cutting vibration signals are determined as monitoring signals and an experimental scheme based on the multifeed rate is designed. The error rate of HMM-SVM is further reduced by introducing the stratification factor comparison method rather than using the conventional probability comparison method. A five-step iterative algorithm is presented to select and optimize the training set, which effectively solves the problems of precision degradation and training data insufficiency. Experimental studies show that the proposed model can accurately predict the surface roughness in case of missing samples. The advantages of the proposed model over least squares support vector machine (LSSVM) and multiple regression approaches are demonstrated via statistical analysis. © 2015 IEEE.

KeywordHard Turning Hidden Markov Model (Hmm) Least Squares Support Vector Machine (Lssvm) Surface Roughness Prediction
DOI10.1109/TASE.2014.2369478
URLView the original
Language英语
WOS Research AreaAutomation & Control Systems
WOS SubjectAutomation & Control Systems
WOS IDWOS:000358585200027
The Source to ArticleScopus
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Cited Times [WOS]:12   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Affiliation1.School of Mechanical Engineering, Southeast University, Nanjing, China
2.Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macao, China
3.School of Mechanical Engineering, Southeast University, Nanjing, China
Recommended Citation
GB/T 7714
He K.,Xu Q.,Jia M.. Modeling and Predicting Surface Roughness in Hard Turning Using a Bayesian Inference-Based HMM-SVM Model[J]. IEEE Transactions on Automation Science and Engineering,2015,12(3):1092.
APA He K.,Xu Q.,&Jia M..(2015).Modeling and Predicting Surface Roughness in Hard Turning Using a Bayesian Inference-Based HMM-SVM Model.IEEE Transactions on Automation Science and Engineering,12(3),1092.
MLA He K.,et al."Modeling and Predicting Surface Roughness in Hard Turning Using a Bayesian Inference-Based HMM-SVM Model".IEEE Transactions on Automation Science and Engineering 12.3(2015):1092.
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