Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network
Abstract Pneumatic 3D-nanomaterial printing, a prominent additive manufacturing technique, excels in processing advanced materials like MXene, crucial for applications in nano-energy, flexible electronics, and sensors. A key challenge in this domain is optimizing process parameters—applied pressure,...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Discover Nano |
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| Online Access: | https://doi.org/10.1186/s11671-024-04155-w |
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| author | Anita Ghandehari Jorge A. Tavares-Negrete Jerome Rajendran Qian Yi Rahim Esfandyarpour |
| author_facet | Anita Ghandehari Jorge A. Tavares-Negrete Jerome Rajendran Qian Yi Rahim Esfandyarpour |
| author_sort | Anita Ghandehari |
| collection | DOAJ |
| description | Abstract Pneumatic 3D-nanomaterial printing, a prominent additive manufacturing technique, excels in processing advanced materials like MXene, crucial for applications in nano-energy, flexible electronics, and sensors. A key challenge in this domain is optimizing process parameters—applied pressure, ink concentration, nozzle diameter, and printing velocity—to achieve uniform, high-quality prints with the desired filament diameter. Traditional trial-and-error methods often result in significant material waste and time consumption. To address this, our study introduces a comprehensive pipeline that initially assesses whether the selected process parameters yield uniform, high-quality MXene prints. Subsequently, it employs a Physics-Guided Artificial Neural Network (PGANN) to predict the filament diameter based on these parameters, integrating fundamental physical principles of the printing process with experimental data. Our findings demonstrate that using an XGBoost classifier, we can classify printed filament quality with an accuracy of 90.44%. Furthermore, the PGANN model shows exceptional performance in predicting the filament diameter, achieving a Pearson Correlation Coefficient (PCC) of 0.9488, a Mean Squared Error (MSE) of 0.000092 mm2, and a Mean Absolute Error (MAE) of 0.00711 mm. This pipeline significantly streamlines the process for researchers, facilitating the selection of optimal printing parameters to consistently achieve high-quality prints and accurately produce the desired filament diameter tailored to specific applications. |
| format | Article |
| id | doaj-art-dbb051419c2d462f8e9b0a54f0bf8b7a |
| institution | Kabale University |
| issn | 2731-9229 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Nano |
| spelling | doaj-art-dbb051419c2d462f8e9b0a54f0bf8b7a2024-12-22T12:43:03ZengSpringerDiscover Nano2731-92292024-12-0119111710.1186/s11671-024-04155-wOptimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural networkAnita Ghandehari0Jorge A. Tavares-Negrete1Jerome Rajendran2Qian Yi3Rahim Esfandyarpour4Department of Electrical Engineering and Computer Science, University of CaliforniaHenry Samueli School of Engineering, University of CaliforniaDepartment of Electrical Engineering and Computer Science, University of CaliforniaDepartment of Electrical Engineering and Computer Science, University of CaliforniaDepartment of Electrical Engineering and Computer Science, University of CaliforniaAbstract Pneumatic 3D-nanomaterial printing, a prominent additive manufacturing technique, excels in processing advanced materials like MXene, crucial for applications in nano-energy, flexible electronics, and sensors. A key challenge in this domain is optimizing process parameters—applied pressure, ink concentration, nozzle diameter, and printing velocity—to achieve uniform, high-quality prints with the desired filament diameter. Traditional trial-and-error methods often result in significant material waste and time consumption. To address this, our study introduces a comprehensive pipeline that initially assesses whether the selected process parameters yield uniform, high-quality MXene prints. Subsequently, it employs a Physics-Guided Artificial Neural Network (PGANN) to predict the filament diameter based on these parameters, integrating fundamental physical principles of the printing process with experimental data. Our findings demonstrate that using an XGBoost classifier, we can classify printed filament quality with an accuracy of 90.44%. Furthermore, the PGANN model shows exceptional performance in predicting the filament diameter, achieving a Pearson Correlation Coefficient (PCC) of 0.9488, a Mean Squared Error (MSE) of 0.000092 mm2, and a Mean Absolute Error (MAE) of 0.00711 mm. This pipeline significantly streamlines the process for researchers, facilitating the selection of optimal printing parameters to consistently achieve high-quality prints and accurately produce the desired filament diameter tailored to specific applications.https://doi.org/10.1186/s11671-024-04155-wAdditive manufacturing3D-nanomaterial printingMXeneProcess parametersPhysics-guided artificial neural networkMachine learning |
| spellingShingle | Anita Ghandehari Jorge A. Tavares-Negrete Jerome Rajendran Qian Yi Rahim Esfandyarpour Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network Discover Nano Additive manufacturing 3D-nanomaterial printing MXene Process parameters Physics-guided artificial neural network Machine learning |
| title | Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network |
| title_full | Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network |
| title_fullStr | Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network |
| title_full_unstemmed | Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network |
| title_short | Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network |
| title_sort | optimization of process parameters in 3d nanomaterials printing for enhanced uniformity quality and dimensional precision using physics guided artificial neural network |
| topic | Additive manufacturing 3D-nanomaterial printing MXene Process parameters Physics-guided artificial neural network Machine learning |
| url | https://doi.org/10.1186/s11671-024-04155-w |
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