# Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques
## 3 Methodology  
### Ann  
An artificial neuron network (ANN) is one of the most powerful techniques for prediction purposes. ANN model learned from the input data and made accurate predictions of future results. ANN technique works on the same principle as our human intelligence works. ANN develops a relationship between input and output parameters from previous trends and experience [13]. In addition, ANN contains several hidden layers and neurons connected according to the complexity of parameters [14]. Figure 3 shows a vector flow diagram of the developed ANN model.  
The general ANN structure contains the input layer, hidden layers, neurons, weights, activation function, and output layer [49]. The ANN algorithms first pick random data to build a relationship between the input parameters and neurons with different weights and activation functions. The ANN algorithm starts with the interlink neurons with different weights and continuously builds relationships to reach output values from training data. The weight in the ANN algorithm controls various input parameters on neurons [13]. Weights are multiplied with each neuron's input and the activation function triggers the output of the neurons [50]. The activation function is a mathematical mechanism that passes information to subsequent neurons. An individual neuron works only when the activation function is triggered. Several activation functions are utilized in ANN algorithms, such as linear, Gaussian, sigmoid, hyperbolic tangent, saturated linear, etc. The sigmoid is the most used function in ANN, as shown in Figure 4[51].  
The main challenge in the ANN model is over-fitting, where the model continuously tries to match the outputs of the training dataset. The over-fitting may occur when more numbers of neurons learn relationships from data for a more extended period. The over-fitted model worked well with a known data set. However, the over-fitted model cannot predict an outcome with good accuracy for the unknown data set [52]. ANN is mainly used where the relationships between input and output data are very complex. The ANN algorithm learns complex interactions and helps to predict the data without giving any significant interpretation. Hence, it is also termed as Black box method. ANN is mainly used nowadays in sophisticated applications such as self-driving cars, speech, and handwriting recognition, and weather forecasting [13]. Researchers recently started exploring the ANN model for complex manufacturing processes to save material, time, and cost. This is especially true for the additive manufacturing process, where many parameters are directly or indirectly involved in the building part. Researchers recently reported the application of the ANN model for the selective laser sintering (SLS) process [53; 54].  
The ANN helps to optimize process parameters for newly developed materials to achieve desired mechanical properties. [PERSON] et al. [55] used the ANN model to predict the effectiveness of the powder spreader for PBF of Ti-6 Al-4V with 96% accuracy. [PERSON] et al. [18] predicted the density of a Nylon-36 with 93% accuracy. [PERSON] et al. [50] also used the ANN model to predict thermal conductivity with the minimum error of predicted vs. actual value of 1.26%.  
Figure 4: Sigmoid function for ANN.  
Figure 3: Diagram of ANN model.