In order to meet the technical requirements of grinding the circumferential cutting edge of indexable inserts, thermo-mechanical properties of bowl-shaped grinding wheel in high speed grinding process and the influenc...In order to meet the technical requirements of grinding the circumferential cutting edge of indexable inserts, thermo-mechanical properties of bowl-shaped grinding wheel in high speed grinding process and the influence of dimension variations of the grinding wheel on machining accuracy were investigated. Firstly, the variation trends of the dimension due to centrifugal force generated in different wheel speeds were studied and the effect of stress stiffening and spin softening was presented. Triangular heat flux distribution model was adopted to determine temperature distribution in grinding process. Temperature field cloud pictures were obtained by the finite element software. Then, dimension variation trends of wheel structure were acquired by considering the thermo-mechanical characteristic under combined action of centrifugal force and grinding heat at different speeds. A method of online dynamic monitoring and automatic compensation for dimension error of indexable insert was proposed. By experimental verification, the precision of the inserts satisfies the requirement of processing.展开更多
Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivate...Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.展开更多
基金Project(2010ZX04001-162)supported by the National Science and Technology Major Project of China
文摘In order to meet the technical requirements of grinding the circumferential cutting edge of indexable inserts, thermo-mechanical properties of bowl-shaped grinding wheel in high speed grinding process and the influence of dimension variations of the grinding wheel on machining accuracy were investigated. Firstly, the variation trends of the dimension due to centrifugal force generated in different wheel speeds were studied and the effect of stress stiffening and spin softening was presented. Triangular heat flux distribution model was adopted to determine temperature distribution in grinding process. Temperature field cloud pictures were obtained by the finite element software. Then, dimension variation trends of wheel structure were acquired by considering the thermo-mechanical characteristic under combined action of centrifugal force and grinding heat at different speeds. A method of online dynamic monitoring and automatic compensation for dimension error of indexable insert was proposed. By experimental verification, the precision of the inserts satisfies the requirement of processing.
基金Supported by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)+1 种基金the Natural Science Foundation of Zhejiang Province(LQ15F030006)and the Science and Technology Program Project of Zhejiang Province(2015C33033)
文摘Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.
