A data-driven machine learning approach for the 3D printing process optimisation
3D printing has become highly applicable in modern life recently. The industry has brought a facelift to most others. However, this technology still exists some shortcomings, and it therefore has not been generalised to bring the best benefits to users. In this paper, based on multilayer perceptron...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-10-01
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| Series: | Virtual and Physical Prototyping |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17452759.2022.2068446 |
| _version_ | 1797678655507267584 |
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| author | Phuong Dong Nguyen Thanh Q. Nguyen Q. B. Tao Frank Vogel H. Nguyen-Xuan |
| author_facet | Phuong Dong Nguyen Thanh Q. Nguyen Q. B. Tao Frank Vogel H. Nguyen-Xuan |
| author_sort | Phuong Dong Nguyen |
| collection | DOAJ |
| description | 3D printing has become highly applicable in modern life recently. The industry has brought a facelift to most others. However, this technology still exists some shortcomings, and it therefore has not been generalised to bring the best benefits to users. In this paper, based on multilayer perceptron and convolution neural network models, we propose a new data-driven machine learning platform for predicting optimised parameters of the 3D printing process from a model design to a complete product. This finding can open up great advances in the current 3D printing technology. Accordingly, the results obtained allow us to predict quickly and accurately some decisive parameters of the traditional 3D printing process such as time, weight and length while the input was fuzzy with a part of the initial information missing. The proposed approach does not need to account for the shape, size and material of the printed object, but it can perform the process automatically without other extra factors. After completing the model, a configurator is proposed to set the parameters for the respective printer types, which makes the 3D printing process simple and fast. |
| first_indexed | 2024-03-11T23:03:05Z |
| format | Article |
| id | doaj.art-eb8f49491b86485287661d1040db1dad |
| institution | Directory Open Access Journal |
| issn | 1745-2759 1745-2767 |
| language | English |
| last_indexed | 2024-03-11T23:03:05Z |
| publishDate | 2022-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj.art-eb8f49491b86485287661d1040db1dad2023-09-21T14:38:03ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672022-10-0117476878610.1080/17452759.2022.20684462068446A data-driven machine learning approach for the 3D printing process optimisationPhuong Dong Nguyen0Thanh Q. Nguyen1Q. B. Tao2Frank Vogel3H. Nguyen-Xuan4CIRTech InstituteThu Dau Mot UniversityThe University of Danang - University of Science and TechnologyInuTech GmbhCIRTech Institute3D printing has become highly applicable in modern life recently. The industry has brought a facelift to most others. However, this technology still exists some shortcomings, and it therefore has not been generalised to bring the best benefits to users. In this paper, based on multilayer perceptron and convolution neural network models, we propose a new data-driven machine learning platform for predicting optimised parameters of the 3D printing process from a model design to a complete product. This finding can open up great advances in the current 3D printing technology. Accordingly, the results obtained allow us to predict quickly and accurately some decisive parameters of the traditional 3D printing process such as time, weight and length while the input was fuzzy with a part of the initial information missing. The proposed approach does not need to account for the shape, size and material of the printed object, but it can perform the process automatically without other extra factors. After completing the model, a configurator is proposed to set the parameters for the respective printer types, which makes the 3D printing process simple and fast.http://dx.doi.org/10.1080/17452759.2022.2068446additive manufacturing3d printingmultilayer perceptronmachine learningconvolutional neural networks |
| spellingShingle | Phuong Dong Nguyen Thanh Q. Nguyen Q. B. Tao Frank Vogel H. Nguyen-Xuan A data-driven machine learning approach for the 3D printing process optimisation Virtual and Physical Prototyping additive manufacturing 3d printing multilayer perceptron machine learning convolutional neural networks |
| title | A data-driven machine learning approach for the 3D printing process optimisation |
| title_full | A data-driven machine learning approach for the 3D printing process optimisation |
| title_fullStr | A data-driven machine learning approach for the 3D printing process optimisation |
| title_full_unstemmed | A data-driven machine learning approach for the 3D printing process optimisation |
| title_short | A data-driven machine learning approach for the 3D printing process optimisation |
| title_sort | data driven machine learning approach for the 3d printing process optimisation |
| topic | additive manufacturing 3d printing multilayer perceptron machine learning convolutional neural networks |
| url | http://dx.doi.org/10.1080/17452759.2022.2068446 |
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