Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during...Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during the metal AM process,which exhibits strong nonlinearities,localized high gradients,and rapid cooling rates.Therefore,real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality,which poses surprising challenges for numerical methods,as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations.In this study,we proposed an isothermal surface imaging and transfer learning framework for fast prediction of isothermal surfaces,which are further used to reconstruct the high-dimensional,nonlinear temperature field.It consists of three key parts:physics-guided isothermal surface imaging to reduce the problem dimensionality by transforming the unstructured temperature field into a series of structured grayscale images,a pre-trained hybrid parameter-to-image generative neural network for the isothermal surface prediction in favor of small training samples,and a transfer learning strategy leveraging physical similarity of these isothermal surfaces in the metal AM process to obtain the 3D temperature field.The training samples are generated using a high-fidelity numerical model,which is validated against experimental data.The predicted results from the proposed framework agree well with those from the high-fidelity numerical simulation for a given combination of process parameters,achieving a computational cost measured in seconds.It is expected that the proposed framework could serve as a powerful tool for predicting the temperature field and further facilitating online control of process parameters.展开更多
Lattice structures can be designed to achieve unique mechanical properties and have attracted increasing attention for applications in high-end industrial equipment,along with the advances in additive manufacturing(AM...Lattice structures can be designed to achieve unique mechanical properties and have attracted increasing attention for applications in high-end industrial equipment,along with the advances in additive manufacturing(AM)technologies.In this work,a novel design of plate lattice structures described by a parametric model is proposed to enrich the design space of plate lattice structures with high connectivity suitable for AM processes.The parametric model takes the basic unit of the triple periodic minimal surface(TPMS)lattice as a skeleton and adopts a set of generation parameters to determine the plate lattice structure with different topologies,which takes the advantages of both plate lattices for superior specific mechanical properties and TPMS lattices for high connectivity,and therefore is referred to as a TPMS-like plate lattice(TLPL).Furthermore,a data-driven shape optimization method is proposed to optimize the TLPL structure for maximum mechanical properties with or without the isotropic constraints.In this method,the genetic algorithm for the optimization is utilized for global search capability,and an artificial neural network(ANN)model for individual fitness estimation is integrated for high efficiency.A set of optimized TLPLs at different relative densities are experimentally validated by the selective laser melting(SLM)fabricated samples.It is confirmed that the optimized TLPLs could achieve elastic isotropy and have superior stiffness over other isotropic lattice structures.展开更多
The reproducing kernel particle method （RKPM） has been efficiently applied to problems with large deformations, high gradients and high modal density. In this paper, it is extended to solve a nonlocal problem modele...The reproducing kernel particle method （RKPM） has been efficiently applied to problems with large deformations, high gradients and high modal density. In this paper, it is extended to solve a nonlocal problem modeled by a fractional advectiondiffusion equation （FADE）, which exhibits a boundary layer with low regularity. We formulate this method on a moving least-square approach. Via the enrichment of fractional-order power functions to the traditional integer-order basis for RKPM, leading terms of the solution to the FADE can be exactly reproduced, which guarantees a good approximation to the boundary layer. Numerical tests are performed to verify the proposed approach.展开更多
In a bird strike, the bird undergoes large deformation like flows; while most part of the structure is in small deformation, the region near the impact point may experience large deformations, even fail. This paper de...In a bird strike, the bird undergoes large deformation like flows; while most part of the structure is in small deformation, the region near the impact point may experience large deformations, even fail. This paper develops a coupled shell-material point method （CSMPM） for bird strike simulation, in which the bird is modeled by the material point method （MPM） and the aircraft structure is modeled by the Belytschko-Lin-Tsay shell element. The interaction between the bird and the structure is handled by a particle-to-surface contact algorithm. The distorted and failed shell elements will be eroded if a certain criterion is reached. The proposed CSMPM takes full advantages of both the finite element method and the MPM for bird strike simulation and is validated by several numerical examples.展开更多
As a Lagrangian meshless method, the material point method （MPM） is suitable for dynamic problems with extreme deformation, but its efficiency and accuracy are not as good as that of the finite element method （FEM...As a Lagrangian meshless method, the material point method （MPM） is suitable for dynamic problems with extreme deformation, but its efficiency and accuracy are not as good as that of the finite element method （FEM） for small deformation problems. Therefore, an algorithm for the coupling of FEM and MPM is proposed to take advantages of both methods. Furthermore, a conversion scheme of elements to particles is developed. Hence, the material domain is firstly discretized by finite elements, and then the distorted elements are automatically converted into MPM particles to avoid element entanglement. The interaction between finite elements and MPM particles is implemented based on the background grid in MPM framework. Numerical results are in good agreement with that of both FEM and MPM展开更多
This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing （AM） materials, including using data-mining techniques to close the cycle of desig...This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing （AM） materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process- structure relationship, the multi-scale multi-physics pro- cess modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high- efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.展开更多
基金funded by the National Natural Science Foundation of China under Grant No.11972086the Fundamental Research Funds for the Central Universities。
文摘Metal additive manufacturing(AM)technology has promising applications across many fields due to its near-net-shape advantages.The quality of the as-built component is closely linked to the temperature evolution during the metal AM process,which exhibits strong nonlinearities,localized high gradients,and rapid cooling rates.Therefore,real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality,which poses surprising challenges for numerical methods,as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations.In this study,we proposed an isothermal surface imaging and transfer learning framework for fast prediction of isothermal surfaces,which are further used to reconstruct the high-dimensional,nonlinear temperature field.It consists of three key parts:physics-guided isothermal surface imaging to reduce the problem dimensionality by transforming the unstructured temperature field into a series of structured grayscale images,a pre-trained hybrid parameter-to-image generative neural network for the isothermal surface prediction in favor of small training samples,and a transfer learning strategy leveraging physical similarity of these isothermal surfaces in the metal AM process to obtain the 3D temperature field.The training samples are generated using a high-fidelity numerical model,which is validated against experimental data.The predicted results from the proposed framework agree well with those from the high-fidelity numerical simulation for a given combination of process parameters,achieving a computational cost measured in seconds.It is expected that the proposed framework could serve as a powerful tool for predicting the temperature field and further facilitating online control of process parameters.
基金Project supported by the National Natural Science Foundation of China (No.11972086)。
文摘Lattice structures can be designed to achieve unique mechanical properties and have attracted increasing attention for applications in high-end industrial equipment,along with the advances in additive manufacturing(AM)technologies.In this work,a novel design of plate lattice structures described by a parametric model is proposed to enrich the design space of plate lattice structures with high connectivity suitable for AM processes.The parametric model takes the basic unit of the triple periodic minimal surface(TPMS)lattice as a skeleton and adopts a set of generation parameters to determine the plate lattice structure with different topologies,which takes the advantages of both plate lattices for superior specific mechanical properties and TPMS lattices for high connectivity,and therefore is referred to as a TPMS-like plate lattice(TLPL).Furthermore,a data-driven shape optimization method is proposed to optimize the TLPL structure for maximum mechanical properties with or without the isotropic constraints.In this method,the genetic algorithm for the optimization is utilized for global search capability,and an artificial neural network(ANN)model for individual fitness estimation is integrated for high efficiency.A set of optimized TLPLs at different relative densities are experimentally validated by the selective laser melting(SLM)fabricated samples.It is confirmed that the optimized TLPLs could achieve elastic isotropy and have superior stiffness over other isotropic lattice structures.
基金supported partly by the National Natural Science Foundation of China (Grant 11521202)support from the Chinese Scholarship Councilpartially support by an Army Research Office (Grant W911NF-15-10569)
文摘The reproducing kernel particle method （RKPM） has been efficiently applied to problems with large deformations, high gradients and high modal density. In this paper, it is extended to solve a nonlocal problem modeled by a fractional advectiondiffusion equation （FADE）, which exhibits a boundary layer with low regularity. We formulate this method on a moving least-square approach. Via the enrichment of fractional-order power functions to the traditional integer-order basis for RKPM, leading terms of the solution to the FADE can be exactly reproduced, which guarantees a good approximation to the boundary layer. Numerical tests are performed to verify the proposed approach.
基金Supported by the National Natural Science Foundation of China(11390363)
文摘In a bird strike, the bird undergoes large deformation like flows; while most part of the structure is in small deformation, the region near the impact point may experience large deformations, even fail. This paper develops a coupled shell-material point method （CSMPM） for bird strike simulation, in which the bird is modeled by the material point method （MPM） and the aircraft structure is modeled by the Belytschko-Lin-Tsay shell element. The interaction between the bird and the structure is handled by a particle-to-surface contact algorithm. The distorted and failed shell elements will be eroded if a certain criterion is reached. The proposed CSMPM takes full advantages of both the finite element method and the MPM for bird strike simulation and is validated by several numerical examples.
基金supported by the National Basic Research Program of China (2010CB832701)
文摘As a Lagrangian meshless method, the material point method （MPM） is suitable for dynamic problems with extreme deformation, but its efficiency and accuracy are not as good as that of the finite element method （FEM） for small deformation problems. Therefore, an algorithm for the coupling of FEM and MPM is proposed to take advantages of both methods. Furthermore, a conversion scheme of elements to particles is developed. Hence, the material domain is firstly discretized by finite elements, and then the distorted elements are automatically converted into MPM particles to avoid element entanglement. The interaction between finite elements and MPM particles is implemented based on the background grid in MPM framework. Numerical results are in good agreement with that of both FEM and MPM
基金Acknowledgements W. Liu and W. Yan acknowledge the support by the National Institute of Standards and Technology （NIST） and Center for Hierarchical Materials Design （CHiMaD） （Grant Nos. 70NANB13H194 and 70NANBI4H012）. S. Lin and O. L. Kafka acknowledge the support of the National Science Foundation Graduate Research Fellowship （Grant No. DGE-1324585）.
文摘This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing （AM） materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process- structure relationship, the multi-scale multi-physics pro- cess modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high- efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.
