# A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review
## 4 Deep Learning Models in Additive Manufacturing
### Generative Adversarial Networks (GANs) and Autoencoder  
The GAN has recently gained popularity in the fields of computer science and manufacturing, ranking among the most widely used deep learning approaches. The GANs are used in computer vision applications in many areas such as medical, and industrial automation. The generating network and the discriminative network are the two networks that make up a GAN [13]. The generator generates the fake image and the decimator differentiates the fake image from the original image. First, using a generator, we create a fake image out of a batch of random vectors drawn from a Gaussian distribution. The generated image does not mirror the real input distribution because the generator has not been educated. We feed the discriminator batches of actual and created fake images from the input distribution so that it can learn to distinguish between the two types of images. An image-enhancement generative adversarial network (IEGAN) is created, and the training procedure uses a new objective function. The thermal images obtained from an AM method are used for image segmentation to confirm the superiority and viability of the proposed IEGAN. Results of experiments show that the created IEGAN works better than the original GAN in raising the contrast ratio of thermal images [27]. Figure 11 depicts the GAN and autoencoder overview. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **AM Procedure** & **Problem** & **Outcome** & **References** \\ \hline \multirow{8}{*}{LSTM} & \multirow{8}{*}{DED} & To determine the temperature of the molten pool, analytical and numerical methods have been developed; however, since the real-time melt pool temperature distribution is not taken into account, the accuracy of these methods is rather low. & Developed a machine learning-based data-driven predictive algorithm to accurately estimate the melt pool temperature during DED. & [90] \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1} CNN, LSTM & \multirow{2}{*}{DED} & Forecasting melt pool temperature is layer-by-layer. & By combining CNN and LSTM networks, geographical and temporal information may be retrieved from melt pool temperature data. & [91] \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{8}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{8}{*}{SLS} & Several factors determine the energy consumption of AM systems. These aspects include traits with multiple dimensions and structures, making them difficult to examine. & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{8}{*}{FDM} & It is investigated how equipment operating conditions affect the quality of the generated products using standard data features from the printer’s sensor signals (vibration, current, etc.). & & \\ \cline{1-1}  & & During the printing process to avoid an uneven and harsh temperature distribution across the printing plate & Anticipate temperature gradient distributions during the printing process & [95] \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._An autoencoder is used for unsupervised learning data encodings. An autoencoder trains the network to identify the key elements of the input image to learn a lower-dimensional representation (encoding) for higher-dimensional data, generally for dimensionality reduction. Ironically, the bottleneck is the most crucial component of the neural network. The autoencoder is widely applied in noise reductions in the image. The auto-encode takes X as input and tries to generate X as output [96]. Table 6 summarizes the various literature on GAN and autoencoders.