G-DfAM: a methodological proposal of generative design for additive manufacturing in the automotive industry

被引:0
作者
Tristan Briard
Frédéric Segonds
Nicolo Zamariola
机构
[1] HESAM Université,Arts et Métiers Institute of Technology, LCPI
[2] ArcelorMittal,undefined
[3] Global Research & Development,undefined
来源
International Journal on Interactive Design and Manufacturing (IJIDeM) | 2020年 / 14卷
关键词
Generative design; Additive manufacturing; Design methodology; Design for additive manufacturing;
D O I
暂无
中图分类号
学科分类号
摘要
Metal additive manufacturing is an emerging technology in the industry and has a great potential. Moreover, new technologies like generative design can maximize this potential by computing complex optimized parts for additive manufacturing solutions. However, there is a lack of methodologies combining both recent and promising technologies. This paper first establishes a state of the art of design for additive manufacturing (DfAM) and generative design methodologies. Then it proposes a generic workflow for generative design tools and it proposes a challenge approach to develop a new DfAM method including generative design tools. Finally, a global 4-step methodology maximizing the potential of generative design and additive manufacturing, the G-DfAM method, is presented and validated through an automotive use case.
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页码:875 / 886
页数:11
相关论文
共 38 条
[1]  
Frazier WE(2014)Metal additive manufacturing: a review J. Mater. Eng. Perform. 23 1917-1928
[2]  
Kuo TC(2001)Design for manufacture and design for ‘X’: concepts, applications, and perspectives Comput. Ind. Eng. 41 241-260
[3]  
Huang SH(2009)Design methodologies: industrial and educational application CIRP Ann. Manuf. Technol. 58 543-565
[4]  
Zhang HC(2018)Design by additive manufacturing: an application in aeronautics and defense Virtual Phys. Prototyp. 13 237-245
[5]  
Tomiyama T(2015)Assembly based methods to support product innovation in design for additive manufacturing: an exploratory case study J. Mech. Des. 137 121701-55
[6]  
Gu P(2017)Holistic approach for industrializing AM technology: from part selection to test and verification Prog. Addit. Manuf. 2 43-724
[7]  
Jin Y(2018)A generative design technique for exploring shape variations Adv. Eng. Inform. 38 712-100
[8]  
Segonds F(2011)A practical generative design method Comput. Aided Des. 43 88-311
[9]  
Laverne F(2008)Genetic evolutionary structural optimization J. Constr. Steel 64 305-40
[10]  
Segonds F(2014)A nodal variable ESO (BESO) method for structural topology optimization Finite Elem. Anal. Des. 86 34-748