# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 2 Machine Learning
### Supervised Learning
A typical CNN architecture (shown in Figure 2) comprises convolutional, max-pooling, and fully connected layers, collectively processing input data to make predictions for either regression or classification. Convolutional layers utilize sets of learnable filers or kernels that traverse the input data, performing element-wise multiplication and summation operations, followed by non-linear activation functions. These operations yield feature maps that capture intricate patterns and feature presentations within the input data. Max-pooling layers are often employed after convolutional layers to downsample the feature maps, effectively reducing the spatial dimensions of the data. Subsequently, the flattened feature maps, as a one-dimensional array, are passed through fully connected layers whose architecture mirrors that of ANNs, for prediction. During training, the weights and biases of a CNN are iteratively adjusted using optimization algorithms such as gradient descent and backpropagation. This adjustment process minimizes the disparity between predicted outputs and actual targets, enhancing the models' performance. Recurrent neural networks (RNNs) constitute another vital class of DL algorithms tailored for handling sequential data, such as text [74]. Unlike standard ANNs, which process a sequence of inputs independently, RNNs maintain an internal state to retain the memory of past inputs. This dynamic temporal behavior allows them to manage sequences of varying lengths effectively. By sharing network weights across different time steps, RNNs excel at learning patterns within sequential data, making them particularly useful in Natural Language Processing (NLP) tasks like speech recognition and text generation. Furthermore, when combined with CNNs, RNNs can even process video data from time-series images. However, traditional RNNs encounter a significant hurdle known as the vanishing gradient problem, where gradients diminish exponentially over long sequences, making it challenging to learn long-term dependencies [75]. To mitigate this issue, innovative architectures like long short-term memory (LSTM) and gated recurrent units (GRUs) have been developed [76; 77]. LSTMs employ specialized gates--input, forget, and output gates--to regulate the flow of information into and out of the memory cells, facilitating selective retention or discard of information over time. On the other hand, GRUs, while similar to LSTMs, feature a simplified structure with only two gates: an update gate and a reset gate. Recently, the transformer architecture has emerged as a groundbreaking solution to the limitations of RNNs and CNNs in processing sequential data [78]. Transformers rely exclusively on self-attention mechanisms to weigh the importance of different input elements when generating outputs. This approach enables transformers to capture long-range dependencies in data more effectively than RNNs and CNNs. Transformers have achieved remarkable success across various NLP tasks, including machine translation and question-answering. Figure 2: The architecture of a CNN.