This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of ir...This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering,the HDT meaning hybridization of physical and digital domains,including deformation and energy efficiency can be built,where the essential parameters can be perceptually predicted in advance,by virtue of the fusion of physical sensors and digital information.The long short term memory(LSTM)can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks,thereby laying a foundation for the HDT.The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation.The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy.The manufacturing efficiency and comprehensive costs are accounted as consideration factors,which are perceptually predicted via LSTM.The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation.The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling(FDM).The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes.The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine(CMM)and 3D optical scanner.The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption,which have been verified with a three-way solenoid valve element,in which the maximum deformation was reduced by 39.78%and the mean absolute percentage error for perceptual prediction was 3.76%.展开更多
This paper presents a customized design method for ergonomic products via additive manufacturing(AM)con-sidering joint biomechanics.An ergonomic customized design model can be built based on kinesiology involving huma...This paper presents a customized design method for ergonomic products via additive manufacturing(AM)con-sidering joint biomechanics.An ergonomic customized design model can be built based on kinesiology involving human joint biomechanics.Manifolds of the human bone can be reconstructed from X-rays,computed tomog-raphy(CT),magnetic resonance imaging(MRI),and direct 3D scanning.The conceptual and detailed design of customized products were implemented on ergonomic shoes and insoles.A lightweight lattice structure with vari-able porosity was generated via structural topology optimization for an ergonomic customized design.Notably,the upper surface of the custom-made insole may adhere perfectly to the plantar surface of the patient,resulting in a lower peak plantar pressure.Finite element analysis(FEA)can be employed to simulate the static or dynamic biomechanical characteristics.The conceptual ergonomic products were forwarded to the machine and fabricated via AM,driven by visual digital twin techniques.The experiments proved that a customized design suitability method for wearable ergonomic products via 3D printing is specifically tailored to the rehabilitation needs of individual customers,while consuming the least cost,time,and materials.展开更多
基金Supported by National Key Research and Development Project of China(Grant No.2022YFB3303303)Zhejiang Provincial Research and Development Project of China(Grant No.LGG22E050010)Key Open Fund of State Key Laboratory of Materials Processing and Die and Mould Technology of China(Grant No.P2024-001).
文摘This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering,the HDT meaning hybridization of physical and digital domains,including deformation and energy efficiency can be built,where the essential parameters can be perceptually predicted in advance,by virtue of the fusion of physical sensors and digital information.The long short term memory(LSTM)can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks,thereby laying a foundation for the HDT.The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation.The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy.The manufacturing efficiency and comprehensive costs are accounted as consideration factors,which are perceptually predicted via LSTM.The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation.The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling(FDM).The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes.The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine(CMM)and 3D optical scanner.The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption,which have been verified with a three-way solenoid valve element,in which the maximum deformation was reduced by 39.78%and the mean absolute percentage error for perceptual prediction was 3.76%.
基金supported by National Key Research and Development Project of China(Grant No.2022YFB3303303)Open Fund of State Key Laboratory of Mechanical Transmissions of China(Grant No.SKLMT-ZDKFKT-202202)+2 种基金Ng Teng Fong Charitable Foundation in the Form of ZJU-SUTD IDEA of China(Grant No.188170-11102)Zhejiang Univer-sity President Special Fund of China(Grant No.2021XZZX008)National Natural Science Foundation of China(Grant Nos.U22A6001,51935009).
文摘This paper presents a customized design method for ergonomic products via additive manufacturing(AM)con-sidering joint biomechanics.An ergonomic customized design model can be built based on kinesiology involving human joint biomechanics.Manifolds of the human bone can be reconstructed from X-rays,computed tomog-raphy(CT),magnetic resonance imaging(MRI),and direct 3D scanning.The conceptual and detailed design of customized products were implemented on ergonomic shoes and insoles.A lightweight lattice structure with vari-able porosity was generated via structural topology optimization for an ergonomic customized design.Notably,the upper surface of the custom-made insole may adhere perfectly to the plantar surface of the patient,resulting in a lower peak plantar pressure.Finite element analysis(FEA)can be employed to simulate the static or dynamic biomechanical characteristics.The conceptual ergonomic products were forwarded to the machine and fabricated via AM,driven by visual digital twin techniques.The experiments proved that a customized design suitability method for wearable ergonomic products via 3D printing is specifically tailored to the rehabilitation needs of individual customers,while consuming the least cost,time,and materials.
