Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor
The fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates the effect of FDM printing process parameters on tensile str...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/5/6/112 |
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| author | Vijaykumar S. Jatti Mandar S. Sapre Ashwini V. Jatti Nitin K. Khedkar Vinaykumar S. Jatti |
| author_facet | Vijaykumar S. Jatti Mandar S. Sapre Ashwini V. Jatti Nitin K. Khedkar Vinaykumar S. Jatti |
| author_sort | Vijaykumar S. Jatti |
| collection | DOAJ |
| description | The fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates the effect of FDM printing process parameters on tensile strength, impact strength, and flexural strength. The effects of process parameters such as printing speed, layer thickness, extrusion temperature, and infill percentage are studied. Polyactic acid (PLA) was used as a filament material for printing test specimens. The experimental layout is designed according to response surface methodology (RSM) and responses are collected. Specimens are prepared for testing of these parameters as per ASTM standards. A mathematical model for each of the responses is developed based on the nonlinear regression method. The desirability approach, nonlinear regression, as well as experimental values are in close agreement with each other. The desirability approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 3.109, 6.532, and 3.712, respectively. The nonlinear regression approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 2.977, 6.532, and 3.474, respectively. The desirability concept and nonlinear regression approach resulted in the best mechanical property of the FDM-printed part. |
| first_indexed | 2024-03-09T17:20:53Z |
| format | Article |
| id | doaj.art-2fdf06a76a48400ca4a0fee0872189b2 |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-09T17:20:53Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-2fdf06a76a48400ca4a0fee0872189b22023-11-24T13:09:45ZengMDPI AGApplied System Innovation2571-55772022-11-015611210.3390/asi5060112Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning RegressorVijaykumar S. Jatti0Mandar S. Sapre1Ashwini V. Jatti2Nitin K. Khedkar3Vinaykumar S. Jatti4Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, IndiaThe fused deposition modelling (FDM) technique involves the deposition of a fused layer of material according to the geometry designed in the software. Several parameters affect the quality of parts produced by FDM. This paper investigates the effect of FDM printing process parameters on tensile strength, impact strength, and flexural strength. The effects of process parameters such as printing speed, layer thickness, extrusion temperature, and infill percentage are studied. Polyactic acid (PLA) was used as a filament material for printing test specimens. The experimental layout is designed according to response surface methodology (RSM) and responses are collected. Specimens are prepared for testing of these parameters as per ASTM standards. A mathematical model for each of the responses is developed based on the nonlinear regression method. The desirability approach, nonlinear regression, as well as experimental values are in close agreement with each other. The desirability approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 3.109, 6.532, and 3.712, respectively. The nonlinear regression approach predicted the tensile strength, impact strength, and flexural strength with a less percentage error of 2.977, 6.532, and 3.474, respectively. The desirability concept and nonlinear regression approach resulted in the best mechanical property of the FDM-printed part.https://www.mdpi.com/2571-5577/5/6/112nonlinear regressionfused deposition modelingdesirability conceptdesign of experimentsresponse surface methodology |
| spellingShingle | Vijaykumar S. Jatti Mandar S. Sapre Ashwini V. Jatti Nitin K. Khedkar Vinaykumar S. Jatti Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor Applied System Innovation nonlinear regression fused deposition modeling desirability concept design of experiments response surface methodology |
| title | Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor |
| title_full | Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor |
| title_fullStr | Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor |
| title_full_unstemmed | Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor |
| title_short | Mechanical Properties of 3D-Printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor |
| title_sort | mechanical properties of 3d printed components using fused deposition modeling optimization using the desirability approach and machine learning regressor |
| topic | nonlinear regression fused deposition modeling desirability concept design of experiments response surface methodology |
| url | https://www.mdpi.com/2571-5577/5/6/112 |
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