Additive manufacturing(AM)technology enables the creation of a wide variety of assemblies and complex shapes from three-dimensional model data in a bottom-up,layer-by-layer manner.Therefore,AM has revolutionized the m...Additive manufacturing(AM)technology enables the creation of a wide variety of assemblies and complex shapes from three-dimensional model data in a bottom-up,layer-by-layer manner.Therefore,AM has revolutionized the modern manufacturing industry,attracting increasing interest from both academic and industrial fields.The Rapid Manufacturing Center(RMC)of the School of Materials Science and Engineering at the Huazhong Univer-sity of Science and Technology(HUST),one of the earliest and most powerful AM research teams in China,has been engaged in AM research since 1991.Aiming to address the“stuck neck”problems of specific high-strength products for AM,the RMC has conducted full-chain research in the aspects of special materials,processes,equip-ment,and applications for AM.Moreover,it has formed a multi-disciplinary research team over the past three decades.Relevant research achievements in the AM field include winning five national awards,more than ten first prizes,and more than ten second prizes at the provincial and ministerial levels.The RMC was complimented as“the world’s most influential organization in the laser AM field in 2018”by Virtual and Physical Prototyping(an international authoritative magazine of AM).Moreover,their industrialization achievements were evaluated as“having affected countries such as Singapore,South Korea,and the United States”by an international author-itative Wohlers Report on AM.In this study,we first summarize the representative research achievements of the RMC in the AM field.These include the preparation and processing technology of high-performance polymeric,metallic,and ceramic materials for AM;advanced processing technology and software/equipment for AM;and typical AM-fabricated products and their applications.Further,we discuss the latest research achievements in cutting-edge 4D printing in terms of feedstock selection,printing processes,induction strategies,and potential ap-plications.Finally,we provide insights into the future directions of AM technology development:(ⅰ)Evolving from three-dimensional printing to multi-dimensional printing,(ⅱ)transitioning from plane slicing to curved surface slicing to woven slicing,(ⅲ)enhancing efficient formation from dot-line-sheet-volume printing,(ⅳ)shifting from single material to multi-materials AM,(ⅴ)advancing from the multiscale direction of macroscopic-mesoscopic-microscopic structures,(ⅵ)integrating material preparation with forming integration,(ⅶ)expanding from small batch to large batch.展开更多
With the rapid development of supercomputers,large-scale computing has become increasingly widespread in various scientific research and engineering fields.Meanwhile,the precision and efficiency of large-scale floatin...With the rapid development of supercomputers,large-scale computing has become increasingly widespread in various scientific research and engineering fields.Meanwhile,the precision and efficiency of large-scale floating-point arithmetic have always been a research hotspot in high-performance computing.This paper studies the numerical method to solve large-scale sparse linear equations,in which the accumulation of rounding errors during the solution process leads to inaccurate results,and large-scale data makes the solver produce a long running time.For the above issues,we use error-free transformation technology and mixed-precision ideas to construct a reliable parallel numerical algorithm framework based on HYPRE,which solves large-scale sparse linear equations to improve accuracy and accelerate numerical calculations.Moreover,we illustrate the implementation details of our technique by implementing two cases.One is that we use error-free transformation technology to design high-precision iterative algorithms,such as GMRES,PCG,and BICGSTAB,which reduce rounding errors in the calculation process and make the result more accurate.The other is that we propose a mixed-precision iterative algorithm that utilizes low-precision formats to achieve higher computing power and reduce computing time.Experimental results demonstrate that XHYPRE has higher reliability and effectiveness.Our XHYPRE is on average 1.3x faster than HYPRE and reduces the number of iterations to 87.1%on average.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.52235008,U2037203,and U2341270)Key Research and Development Plan of Hubei Province(2022BAA030).
文摘Additive manufacturing(AM)technology enables the creation of a wide variety of assemblies and complex shapes from three-dimensional model data in a bottom-up,layer-by-layer manner.Therefore,AM has revolutionized the modern manufacturing industry,attracting increasing interest from both academic and industrial fields.The Rapid Manufacturing Center(RMC)of the School of Materials Science and Engineering at the Huazhong Univer-sity of Science and Technology(HUST),one of the earliest and most powerful AM research teams in China,has been engaged in AM research since 1991.Aiming to address the“stuck neck”problems of specific high-strength products for AM,the RMC has conducted full-chain research in the aspects of special materials,processes,equip-ment,and applications for AM.Moreover,it has formed a multi-disciplinary research team over the past three decades.Relevant research achievements in the AM field include winning five national awards,more than ten first prizes,and more than ten second prizes at the provincial and ministerial levels.The RMC was complimented as“the world’s most influential organization in the laser AM field in 2018”by Virtual and Physical Prototyping(an international authoritative magazine of AM).Moreover,their industrialization achievements were evaluated as“having affected countries such as Singapore,South Korea,and the United States”by an international author-itative Wohlers Report on AM.In this study,we first summarize the representative research achievements of the RMC in the AM field.These include the preparation and processing technology of high-performance polymeric,metallic,and ceramic materials for AM;advanced processing technology and software/equipment for AM;and typical AM-fabricated products and their applications.Further,we discuss the latest research achievements in cutting-edge 4D printing in terms of feedstock selection,printing processes,induction strategies,and potential ap-plications.Finally,we provide insights into the future directions of AM technology development:(ⅰ)Evolving from three-dimensional printing to multi-dimensional printing,(ⅱ)transitioning from plane slicing to curved surface slicing to woven slicing,(ⅲ)enhancing efficient formation from dot-line-sheet-volume printing,(ⅳ)shifting from single material to multi-materials AM,(ⅴ)advancing from the multiscale direction of macroscopic-mesoscopic-microscopic structures,(ⅵ)integrating material preparation with forming integration,(ⅶ)expanding from small batch to large batch.
基金supported by the NuSCAP(ANR-20-CE48-0014)project of the French National Agency for Research(ANR)the 173 program(2020-JCJQ-ZD-029)Science Challenge Project(TZ2016002).
文摘With the rapid development of supercomputers,large-scale computing has become increasingly widespread in various scientific research and engineering fields.Meanwhile,the precision and efficiency of large-scale floating-point arithmetic have always been a research hotspot in high-performance computing.This paper studies the numerical method to solve large-scale sparse linear equations,in which the accumulation of rounding errors during the solution process leads to inaccurate results,and large-scale data makes the solver produce a long running time.For the above issues,we use error-free transformation technology and mixed-precision ideas to construct a reliable parallel numerical algorithm framework based on HYPRE,which solves large-scale sparse linear equations to improve accuracy and accelerate numerical calculations.Moreover,we illustrate the implementation details of our technique by implementing two cases.One is that we use error-free transformation technology to design high-precision iterative algorithms,such as GMRES,PCG,and BICGSTAB,which reduce rounding errors in the calculation process and make the result more accurate.The other is that we propose a mixed-precision iterative algorithm that utilizes low-precision formats to achieve higher computing power and reduce computing time.Experimental results demonstrate that XHYPRE has higher reliability and effectiveness.Our XHYPRE is on average 1.3x faster than HYPRE and reduces the number of iterations to 87.1%on average.
