A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing.

Tao Zhang, Uzair Sajjad, Akash Sengupta, Mubasher Ali, Muhammad Sultan, Khalid Hamid

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Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Tao ZhangSchool of 3D Printing, Xinxiang University, Xinxiang 453003, China.

Uzair SajjadDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Akash SenguptaDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.

Mubasher AliDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China.

Muhammad SultanDepartment of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, Pakistan.ORCID 0000-0002-7301-5567

Khalid HamidDepartment of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway.

Funding

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6 · The paper itself

Abstract

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 × 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 × 10

Indexed as

additive manufacturingdeep learninggenetic algorithmlattice structuretopology optimization

Identifiers

PMID37893361

DOI10.3390/mi14101924

PMCPMC10609015

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