Carbon-fiber-reinforced plastics(CFRP)with improved mechanical properties based on modified epoxy binders were investigated in this study.By adding 15 parts by weight(p.b.w.)of copolymer of polysulfone with cardo phth...Carbon-fiber-reinforced plastics(CFRP)with improved mechanical properties based on modified epoxy binders were investigated in this study.By adding 15 parts by weight(p.b.w.)of copolymer of polysulfone with cardo phthalide group(PSFP-70C)to the epoxyanhydride binder,the flexural strength of the epoxy polymer was increased by 60%,the CFRP based on it by 57%,the flexural modulus of the epoxy polymer was increased by 83%,and the composite by 96%.The adhesion strength of the binder to carbon fiber reached a high level at 10 p.b.w.of thermoplastic modifier and increased by 65%compared to the unmodified binder.Scanning electron microscopy(SEM)was used to determine that in epoxyanhydride systems with a polysulfone content of 5–15 p.b.w.,the structure belongs to the"matrix dispersion"type and with a content of 20 p.b.w.to the"interpenetrating phase"type.A heterogeneous structure was also observed using dynamic mechanical analysis.展开更多
Most failures in component operation occur due to cyclic loads.Validation has been performed under quasistatic loads,but the fatigue life of components under dynamic loads should be predicted to prevent failures durin...Most failures in component operation occur due to cyclic loads.Validation has been performed under quasistatic loads,but the fatigue life of components under dynamic loads should be predicted to prevent failures during component service life.Fatigue is a damage accumulation process where loads degrade the material,depending on the characteristics and number of repetitions of the load.Studies on themechanical fatigue of 3D-printedOnyx are limited.In this paper,the strength of 3D-printed Onyx components under dynamic conditions(repetitive loads)is estimated.Fatigue life prediction is influenced bymanufacturing processes,material properties,and applied loads,which can cause scatter in the results due to the interplay of these factors.By utilizing synthetic parameters derived from mechanical properties,the accuracy of fatigue life predictions has been improved significantly,from 23.13%to 98.33%.Additive manufacturing is flexible,but this flexibility generates scatter in the mechanical properties of produced components.This work also proposes the use of synthetic data with a neural network to improve the fatigue life prediction of printedOnyx subjected to tension–tension loads.Experimental uniaxial loads were used to characterize themechanical behaviorofprinted specimens.The experimental datawereused to evaluate thenumerical predictionsobtainedthrough finite element analysis using commercial software and an artificial neural network.The results showed that the use of synthetic data helped improve fatigue life prediction.展开更多
基金financially supported by the Ministry of Science and Higher Education of the Russian Federation。
文摘Carbon-fiber-reinforced plastics(CFRP)with improved mechanical properties based on modified epoxy binders were investigated in this study.By adding 15 parts by weight(p.b.w.)of copolymer of polysulfone with cardo phthalide group(PSFP-70C)to the epoxyanhydride binder,the flexural strength of the epoxy polymer was increased by 60%,the CFRP based on it by 57%,the flexural modulus of the epoxy polymer was increased by 83%,and the composite by 96%.The adhesion strength of the binder to carbon fiber reached a high level at 10 p.b.w.of thermoplastic modifier and increased by 65%compared to the unmodified binder.Scanning electron microscopy(SEM)was used to determine that in epoxyanhydride systems with a polysulfone content of 5–15 p.b.w.,the structure belongs to the"matrix dispersion"type and with a content of 20 p.b.w.to the"interpenetrating phase"type.A heterogeneous structure was also observed using dynamic mechanical analysis.
文摘Most failures in component operation occur due to cyclic loads.Validation has been performed under quasistatic loads,but the fatigue life of components under dynamic loads should be predicted to prevent failures during component service life.Fatigue is a damage accumulation process where loads degrade the material,depending on the characteristics and number of repetitions of the load.Studies on themechanical fatigue of 3D-printedOnyx are limited.In this paper,the strength of 3D-printed Onyx components under dynamic conditions(repetitive loads)is estimated.Fatigue life prediction is influenced bymanufacturing processes,material properties,and applied loads,which can cause scatter in the results due to the interplay of these factors.By utilizing synthetic parameters derived from mechanical properties,the accuracy of fatigue life predictions has been improved significantly,from 23.13%to 98.33%.Additive manufacturing is flexible,but this flexibility generates scatter in the mechanical properties of produced components.This work also proposes the use of synthetic data with a neural network to improve the fatigue life prediction of printedOnyx subjected to tension–tension loads.Experimental uniaxial loads were used to characterize themechanical behaviorofprinted specimens.The experimental datawereused to evaluate thenumerical predictionsobtainedthrough finite element analysis using commercial software and an artificial neural network.The results showed that the use of synthetic data helped improve fatigue life prediction.
