Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network
This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6860 |
| _version_ | 1797552252803612672 |
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| author | Jinghua Xu Kang Wang Shuyou Zhang Guodong Yi Jianrong Tan Sheng Luo Jihong Pang |
| author_facet | Jinghua Xu Kang Wang Shuyou Zhang Guodong Yi Jianrong Tan Sheng Luo Jihong Pang |
| author_sort | Jinghua Xu |
| collection | DOAJ |
| description | This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements. |
| first_indexed | 2024-03-10T15:57:22Z |
| format | Article |
| id | doaj.art-f24eccc12a4e429898ef2a96b5b9e21a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:57:22Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f24eccc12a4e429898ef2a96b5b9e21a2023-11-20T15:34:39ZengMDPI AGApplied Sciences2076-34172020-09-011019686010.3390/app10196860Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial NetworkJinghua Xu0Kang Wang1Shuyou Zhang2Guodong Yi3Jianrong Tan4Sheng Luo5Jihong Pang6State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaSchool of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, ChinaSchool of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325000, ChinaSchool of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325000, ChinaThis paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements.https://www.mdpi.com/2076-3417/10/19/6860thermal deformation defect predictionlayered printingvirtual printing via digital twinsconvolutional generative adversarial network (CGAN)surface profile errorbinocular stereo vision |
| spellingShingle | Jinghua Xu Kang Wang Shuyou Zhang Guodong Yi Jianrong Tan Sheng Luo Jihong Pang Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network Applied Sciences thermal deformation defect prediction layered printing virtual printing via digital twins convolutional generative adversarial network (CGAN) surface profile error binocular stereo vision |
| title | Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network |
| title_full | Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network |
| title_fullStr | Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network |
| title_full_unstemmed | Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network |
| title_short | Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network |
| title_sort | thermal deformation defect prediction for layered printing using convolutional generative adversarial network |
| topic | thermal deformation defect prediction layered printing virtual printing via digital twins convolutional generative adversarial network (CGAN) surface profile error binocular stereo vision |
| url | https://www.mdpi.com/2076-3417/10/19/6860 |
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