## 8 Conclusions  
(1) A method of thermal Deformation defect deformation prediction for 3 DP using Convolutional Generative Adversarial Network (CGAN) is put forward.  
The multiscale Convolutional Generative Adversarial Network (CGAN) is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The thermal deformation of each layer can be predicted with high precision for various shapes. The CGAN method is especially suitable for complex structures with diversified region-based requirements.  
(2) The depth convolution is used to extract multi-scale features of LCM in each layer.  
The CGAN model takes each layer end-effector temperature, temperature gradient, printing velocity as the optimization parameters and solved MOO using Non-dominated Sorting Genetic Algorithm-II to calculate thermal deformation using minimum residual depth convolution. The average thermal deformation can be decreased via CGAN. The visible primitives including points and facets are obtained based on convex hull principles, which paves the way for virtual printing via Digital Twins technology.  
(3) The proposed CGAN method is verified by physical experiment.  
The proposed method is verified by serialized physical experiments. The thermographs are firstly obtained using thermal field measurement to determine the temperature and temperature difference. The trilinear coordinates measuring instrument and binocular stereo vision laser scanner are then used to determine the actual thermal deformation of the printed objects. Compared with traditional methods, the CGAN method can deal with the thermal deformation with more optimal parameters which contributes to forward design of irregular complex parts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Parameters** & **Before Optimization** & **After Optimization** & **Ratio** \\ \hline Max \((\delta)\) & 2.075594e-02 & 2.695928e-02 & 29.89\% \\ Min \((\delta)\) & 4.795815e-03 & 0 & \(-\)100.00\% \\ Max \((s_{error})\) & 2.605556e-06 & 2.020809e-06 & \(-\)22.44\% \\ Min \((s_{error})\) & 2.390000e-06 & 1.670376e-06 & \(-\)30.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results comparison for various layer amounts.  
Figure 11: Comparison before and after optimization of human knee femur where (**a**) is regarding cusp height and (**b**) is the surface profile error regarding number of training iterations of CGAN.  
In future, encouraged by the artificial intelligence technology, the proposed method will be planned to be more suitable for AM of more complex part structures, such as mechanical grooves, convex shoulders, matching holes, and inner cavity microfluidic channels, supported by knowledgebase big data.  
Software, K.W.; validation, G.Y.; resources, J.P.; visualization, S.L.; supervision, S.Z.; funding acquisition, J.X. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 51935009; 51775494; 51821093), Zhejiang key research and development project (No. 2019C01141; LCG18E050007; LCG20E050006), National key research and development project of China (No. 2018 YFB1700701)  
The authors declare no conflict of interest.