Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network
Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accura...
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MDPI AG
2025-04-01
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| author | Alin-Daniel Rizea Cristina-Florena Banică Tatiana Georgescu Alexandru Sover Daniel-Constantin Anghel |
| author_facet | Alin-Daniel Rizea Cristina-Florena Banică Tatiana Georgescu Alexandru Sover Daniel-Constantin Anghel |
| author_sort | Alin-Daniel Rizea |
| collection | DOAJ |
| description | Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters—layer thickness, infill density, and nominal diameter—on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R<sup>2</sup> values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality. |
| format | Article |
| id | doaj-art-295d455b24a3464b80debea1ea9ace7a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-295d455b24a3464b80debea1ea9ace7a2025-08-20T03:06:31ZengMDPI AGApplied Sciences2076-34172025-04-01157395810.3390/app15073958Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural NetworkAlin-Daniel Rizea0Cristina-Florena Banică1Tatiana Georgescu2Alexandru Sover3Daniel-Constantin Anghel4Department of Manufacturing and Industrial Management, National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Târgul din Vale Street, 110040 Pitesti, RomaniaDoctoral School of Industrial Engineering, National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Târgul din Vale Street, 110040 Pitesti, RomaniaDoctoral School of Industrial Engineering, National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Târgul din Vale Street, 110040 Pitesti, RomaniaDepartment of Engineering, Ansbach University of Applied Sciences, 91522 Ansbach, GermanyDepartment of Manufacturing and Industrial Management, National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Târgul din Vale Street, 110040 Pitesti, RomaniaSplined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters—layer thickness, infill density, and nominal diameter—on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R<sup>2</sup> values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality.https://www.mdpi.com/2076-3417/15/7/3958FDM 3D printingdimensional accuracysplined shafts and hubsartificial neural networksgenetic algorithms |
| spellingShingle | Alin-Daniel Rizea Cristina-Florena Banică Tatiana Georgescu Alexandru Sover Daniel-Constantin Anghel Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network Applied Sciences FDM 3D printing dimensional accuracy splined shafts and hubs artificial neural networks genetic algorithms |
| title | Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network |
| title_full | Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network |
| title_fullStr | Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network |
| title_full_unstemmed | Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network |
| title_short | Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network |
| title_sort | dimensional accuracy analysis of splined shafts and hubs obtained by fused deposition modeling 3d printing using a genetic algorithm and artificial neural network |
| topic | FDM 3D printing dimensional accuracy splined shafts and hubs artificial neural networks genetic algorithms |
| url | https://www.mdpi.com/2076-3417/15/7/3958 |
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