Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study
This research focuses on the relationship between the tensile strength of PLA material and several 3D printing parameters, such as infill density, layer height, print speed, and extrusion temperature, utilizing the Fused Deposition Modeling (FDM) method of Additive Manufacturing (AM). Tensile streng...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2023.1336837/full |
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| author | Vijaykumar S. Jatti Shahid Tamboli Sarfaraj Shaikh Nitin S. Solke Vikas Gulia Vinaykumar S. Jatti Nitin K. Khedkar Sachin Salunkhe Sachin Salunkhe Marek Pagáč Emad S. Abouel Nasr |
| author_facet | Vijaykumar S. Jatti Shahid Tamboli Sarfaraj Shaikh Nitin S. Solke Vikas Gulia Vinaykumar S. Jatti Nitin K. Khedkar Sachin Salunkhe Sachin Salunkhe Marek Pagáč Emad S. Abouel Nasr |
| author_sort | Vijaykumar S. Jatti |
| collection | DOAJ |
| description | This research focuses on the relationship between the tensile strength of PLA material and several 3D printing parameters, such as infill density, layer height, print speed, and extrusion temperature, utilizing the Fused Deposition Modeling (FDM) method of Additive Manufacturing (AM). Tensile strength of the samples was determined in compliance with ASTM D638 standard, and the experiments were carried out according to a planned arrangement. Six distinct methods were used to optimize the tensile strength: Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Simulated Annealing (SA), and Cohort Intelligence (CI). Several runs of the optimization methods demonstrated their consistency in producing the same values of tensile strength, indicating their reliability. The optimization results showed that JAYA performed better than the other algorithms, resulting in a material with the maximum tensile strength of 55.475 N/mm2. Validation experiments were carried out to confirm the efficacy of these algorithms. The results showed that the ideal input parameters produced tensile strength values that closely matched the anticipated values with a low percentage error. The benefits of applying these algorithms to improve the tensile strength of PLA materials for 3D printing are demonstrated by this study, which also offers insightful information about how to optimize FDM procedures. |
| first_indexed | 2024-03-08T09:35:05Z |
| format | Article |
| id | doaj.art-aaa231b62bb34d5cbf17e5edb7c8347c |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-03-08T09:35:05Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-aaa231b62bb34d5cbf17e5edb7c8347c2024-01-30T09:52:33ZengFrontiers Media S.A.Frontiers in Materials2296-80162024-01-011010.3389/fmats.2023.13368371336837Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative studyVijaykumar S. Jatti0Shahid Tamboli1Sarfaraj Shaikh2Nitin S. Solke3Vikas Gulia4Vinaykumar S. Jatti5Nitin K. Khedkar6Sachin Salunkhe7Sachin Salunkhe8Marek Pagáč9Emad S. Abouel Nasr10Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Mechanical Engineering, Symbiosis Institute of Technology, Pune, IndiaDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Mechanical Engineering, Gazi University Faculty of Engineering, Ankara, TürkiyeDepartment of Machining, Assembly and Engineering Technology, Faculty of Mechanical Engineering, Ostrava-Poruba, CzechiaDepartment of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi ArabiaThis research focuses on the relationship between the tensile strength of PLA material and several 3D printing parameters, such as infill density, layer height, print speed, and extrusion temperature, utilizing the Fused Deposition Modeling (FDM) method of Additive Manufacturing (AM). Tensile strength of the samples was determined in compliance with ASTM D638 standard, and the experiments were carried out according to a planned arrangement. Six distinct methods were used to optimize the tensile strength: Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Simulated Annealing (SA), and Cohort Intelligence (CI). Several runs of the optimization methods demonstrated their consistency in producing the same values of tensile strength, indicating their reliability. The optimization results showed that JAYA performed better than the other algorithms, resulting in a material with the maximum tensile strength of 55.475 N/mm2. Validation experiments were carried out to confirm the efficacy of these algorithms. The results showed that the ideal input parameters produced tensile strength values that closely matched the anticipated values with a low percentage error. The benefits of applying these algorithms to improve the tensile strength of PLA materials for 3D printing are demonstrated by this study, which also offers insightful information about how to optimize FDM procedures.https://www.frontiersin.org/articles/10.3389/fmats.2023.1336837/fulltensile strengthfused deposition modelingJAYA algorithmteaching learning based optimizationparticle swarm optimizationcohort intelligence |
| spellingShingle | Vijaykumar S. Jatti Shahid Tamboli Sarfaraj Shaikh Nitin S. Solke Vikas Gulia Vinaykumar S. Jatti Nitin K. Khedkar Sachin Salunkhe Sachin Salunkhe Marek Pagáč Emad S. Abouel Nasr Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study Frontiers in Materials tensile strength fused deposition modeling JAYA algorithm teaching learning based optimization particle swarm optimization cohort intelligence |
| title | Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study |
| title_full | Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study |
| title_fullStr | Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study |
| title_full_unstemmed | Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study |
| title_short | Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study |
| title_sort | optimization of tensile strength in 3d printed pla parts via meta heuristic approaches a comparative study |
| topic | tensile strength fused deposition modeling JAYA algorithm teaching learning based optimization particle swarm optimization cohort intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2023.1336837/full |
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