文献类型：期刊文献

英文题名：A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing

作者：Zhang, Tao[1];Sajjad, Uzair[2];Sengupta, Akash[3];Ali, Mubasher[4];Sultan, Muhammad[5];Hamid, Khalid[6]

第一作者：张涛

通讯作者：Zhang, T[1];Hamid, K[2]

机构：[1]Xinxiang Univ, Sch Printing 3D, Xinxiang 453003, Peoples R China;[2]Natl Taipei Univ Technol, Dept Energy & Refrigerating Air Conditioning Engn, Taipei 10608, Taiwan;[3]Natl Yang Ming Chiao Tung Univ, Dept Mech Engn, 1001 Univ Rd, Hsinchu 300, Taiwan;[4]Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China;[5]Bahauddin Zakariya Univ, Dept Agr Engn, Bosan Rd, Multan 60800, Pakistan;[6]Norwegian Univ Sci & Technol NTNU, Dept Energy & Proc Engn, N-7491 Trondheim, Norway

第一机构：新乡学院

通讯机构：[1]corresponding author), Xinxiang Univ, Sch Printing 3D, Xinxiang 453003, Peoples R China;[2]corresponding author), Norwegian Univ Sci & Technol NTNU, Dept Energy & Proc Engn, N-7491 Trondheim, Norway.|[11071]新乡学院;

年份：2023

卷号：14

期号：10

外文期刊名：MICROMACHINES

收录：;EI(收录号：20234415004387);Scopus(收录号：2-s2.0-85175365620);WOS:【SCI-EXPANDED(收录号:WOS:001090004700001)】；

语种：英文

外文关键词：genetic algorithm; deep learning; additive manufacturing; lattice structure; topology optimization

摘要：This research is centered on optimizing the mechanical properties of additively manufactured (AM) lattice structures via strain optimization by controlling different design and process parameters such as stress, unit cell size, total height, width, and relative density. In this regard, numerous topologies, including sea urchin (open cell) structure, honeycomb, and Kelvin structures simple, round, and crossbar (2 x 2), were considered that were fabricated using different materials such as plastics (PLA, PA12), metal (316L stainless steel), and polymer (thiol-ene) via numerous AM technologies, including stereolithography (SLA), multijet fusion (MJF), fused deposition modeling (FDM), direct metal laser sintering (DMLS), and selective laser melting (SLM). The developed deep-learning-driven genetic metaheuristic algorithm was able to achieve a particular strain value for a considered topology of the lattice structure by controlling the considered input parameters. For instance, in order to achieve a strain value of 2.8 x 10-6 mm/mm for the sea urchin structure, the developed model suggests the optimal stress (11.9 MPa), unit cell size (11.4 mm), total height (42.5 mm), breadth (8.7 mm), width (17.29 mm), and relative density (6.67%). Similarly, these parameters were controlled to optimize the strain for other investigated lattice structures. This framework can be helpful in designing various AM lattice structures of desired mechanical qualities.