A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing
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 topo...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/10/1924 |
| _version_ | 1797572904796291072 |
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| author | Tao Zhang Uzair Sajjad Akash Sengupta Mubasher Ali Muhammad Sultan Khalid Hamid |
| author_facet | Tao Zhang Uzair Sajjad Akash Sengupta Mubasher Ali Muhammad Sultan Khalid Hamid |
| author_sort | Tao Zhang |
| collection | DOAJ |
| description | 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<sup>−6</sup> 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. |
| first_indexed | 2024-03-10T21:02:20Z |
| format | Article |
| id | doaj.art-261eb917a5ca4f8d81f0754692dbca47 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T21:02:20Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-261eb917a5ca4f8d81f0754692dbca472023-11-19T17:24:53ZengMDPI AGMicromachines2072-666X2023-10-011410192410.3390/mi14101924A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive ManufacturingTao Zhang0Uzair Sajjad1Akash Sengupta2Mubasher Ali3Muhammad Sultan4Khalid Hamid5School of 3D Printing, Xinxiang University, Xinxiang 453003, ChinaDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, TaiwanDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, ChinaDepartment of Agricultural Engineering, Bahauddin Zakariya University, Bosan Road, Multan 60800, PakistanDepartment of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, NorwayThis 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<sup>−6</sup> 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.https://www.mdpi.com/2072-666X/14/10/1924genetic algorithmdeep learningadditive manufacturinglattice structuretopology optimization |
| spellingShingle | Tao Zhang Uzair Sajjad Akash Sengupta Mubasher Ali Muhammad Sultan Khalid Hamid A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing Micromachines genetic algorithm deep learning additive manufacturing lattice structure topology optimization |
| title | A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing |
| title_full | A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing |
| title_fullStr | A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing |
| title_full_unstemmed | A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing |
| title_short | A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing |
| title_sort | hybrid data driven metaheuristic framework to optimize strain of lattice structures proceeded by additive manufacturing |
| topic | genetic algorithm deep learning additive manufacturing lattice structure topology optimization |
| url | https://www.mdpi.com/2072-666X/14/10/1924 |
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