Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling
Additive manufacturing has gained popularity among material scientists, researchers, industries, and end users due to the flexible, low cost, and simple manufacturing process. Among number of techniques, fused deposition modeling (FDM) is the most recognized technology due to easy operation, lower e...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Biomaterials |
| Online Access: | http://dx.doi.org/10.1155/2022/4541450 |
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| author | Raman Kumar Jasgurpreet Singh Chohan Sandeep Singh Shubham Sharma Yadvinder Singh S. Rajkumar |
| author_facet | Raman Kumar Jasgurpreet Singh Chohan Sandeep Singh Shubham Sharma Yadvinder Singh S. Rajkumar |
| author_sort | Raman Kumar |
| collection | DOAJ |
| description | Additive manufacturing has gained popularity among material scientists, researchers, industries, and end users due to the flexible, low cost, and simple manufacturing process. Among number of techniques, fused deposition modeling (FDM) is the most recognized technology due to easy operation, lower environmental degradation, and portable apparatus. Despite numerous advantages, the limitations of this technique are poor surface finish, dimensional accuracy, and mechanical strength, which must be improved. The present study focuses on the implementation of the genetic algorithm and Taguchi techniques to achieve minimum dimensional variability of FDM parts especially for polymeric biocomposites. The output has been measured using standard testing techniques followed by Taguchi and genetic algorithm analyses. Four response variables were measured and were converted into single variable with combination of different weightages of each response. Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. The advanced optimization and production techniques have yielded promising results which have been validated by advanced algorithms. It was found that layer thickness and orientation angle were significant parameters which influenced the dimensional accuracy whereas best fitness value was 0.377. |
| format | Article |
| id | doaj-art-fd6b28b9e3784e82baeccb7d4e3018d2 |
| institution | Kabale University |
| issn | 1687-8795 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomaterials |
| spelling | doaj-art-fd6b28b9e3784e82baeccb7d4e3018d22025-08-20T03:25:26ZengWileyInternational Journal of Biomaterials1687-87952022-01-01202210.1155/2022/4541450Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition ModelingRaman Kumar0Jasgurpreet Singh Chohan1Sandeep Singh2Shubham Sharma3Yadvinder Singh4S. Rajkumar5Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Civil EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringAdditive manufacturing has gained popularity among material scientists, researchers, industries, and end users due to the flexible, low cost, and simple manufacturing process. Among number of techniques, fused deposition modeling (FDM) is the most recognized technology due to easy operation, lower environmental degradation, and portable apparatus. Despite numerous advantages, the limitations of this technique are poor surface finish, dimensional accuracy, and mechanical strength, which must be improved. The present study focuses on the implementation of the genetic algorithm and Taguchi techniques to achieve minimum dimensional variability of FDM parts especially for polymeric biocomposites. The output has been measured using standard testing techniques followed by Taguchi and genetic algorithm analyses. Four response variables were measured and were converted into single variable with combination of different weightages of each response. Maximum weightage was given to width of FDM polymeric biocomposite parts which may play critical role in biomedical and aerospace applications. The advanced optimization and production techniques have yielded promising results which have been validated by advanced algorithms. It was found that layer thickness and orientation angle were significant parameters which influenced the dimensional accuracy whereas best fitness value was 0.377.http://dx.doi.org/10.1155/2022/4541450 |
| spellingShingle | Raman Kumar Jasgurpreet Singh Chohan Sandeep Singh Shubham Sharma Yadvinder Singh S. Rajkumar Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling International Journal of Biomaterials |
| title | Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling |
| title_full | Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling |
| title_fullStr | Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling |
| title_full_unstemmed | Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling |
| title_short | Implementation of Taguchi and Genetic Algorithm Techniques for Prediction of Optimal Part Dimensions for Polymeric Biocomposites in Fused Deposition Modeling |
| title_sort | implementation of taguchi and genetic algorithm techniques for prediction of optimal part dimensions for polymeric biocomposites in fused deposition modeling |
| url | http://dx.doi.org/10.1155/2022/4541450 |
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