# Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques
## 2 Literature Review  
The density of the building part influences mechanical and microstructure properties. In additive manufacturing, material densification is the most crucial factor to consider [28]. The optimum range of process parameters is required to achieve a denser product. The part produced with the lower energy density may have unmelted particles, which results in a porous structure. The higher energy density creates a steep \"V\" shaped melt pool and leads to micro evaporation; thus, gas gets trapped below the melt pool and results in porosity. [PERSON] et al. [21] experimentally obtained the energy density levels for minimum and maximum porosity for SS316L as 80 J/mm\({}^{3}\) and 50 J/mm\({}^{3}\), respectively. Hence, optimization of process parameters needs to be explored to get minimum porosity. [PERSON] et al. [24] also investigated the importance of optimum laser energy density for minimum porosity. They observed a similar trend; as energy density increases, porosity increases to a certain level and then decreases. Further, [PERSON] et al. [29] correlated the melt pool shape variation with the porosity. The higher scanning speed creates a tiny size melt pool, leading to a higher porous structure with lower tensile strength. [PERSON] et al. [30] also experimented with the porous structure for bone implants with 40 to 50% porosity which has desired elastic modulus of 26 GPa for the human body. ML can improve the AM process capability in many ways, such as topology optimization, online monitoring, design features optimization, defect detection, and parameter optimization [28]. The application of ML for AM is presented in Figure 2[31]. The various techniques of ML have been utilized in their intelligence to learn complex interactions between numbers of input parameters and the outputs [19; 21]. Most of the work has been reported in areas such as process monitoring, defect detection, and design optimization [32; 33; 34; 35; 36; 37; 38]. [PERSON] et al. [39] used a gaussian-based model for the process planning of SS316L concerning melt pool conditions. [PERSON] et al. [40] also used the Gaussian \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Author** & **Research Area** \\ \hline
1 & [PERSON] A [17] & Controlling Porosity in PBF-AM \\
2 & [PERSON] et al. [20] & Porosity and build rate analysis \\
3 & [PERSON] et al. [21] & Microstructure and Hardness evaluation \\
4 & [PERSON] et al. [22] & Process Parameters Influences \\
5 & [PERSON] et al. [23] & Process Parameter to Part quality \\
6 & [PERSON] et al. [24] & Effect of Process Parameters \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter collection for density of SS316L by AM. Figure 2: Application of ML in additive manufacturing. process for optimizing the post-processing time by controlling the cooling rate. The ML models also recognize the types of defects in AM. The insufficient energy density leads to a lack of fusion defects, whereas high energy density input leads to keyhole porosity. [PERSON] et al. [41] developed a random forest model to predict the keyhole and lack of fusion defect in the sintering of Inconel-718. [PERSON] et al. [42] used a support vector machine (SVM) to classify good and bad layer deposition concerning surface morphology. [PERSON] et al. [43] utilize the ML model to predict the geometrical deviation in the deposited layer. The optimum parameters play a crucial role in enhancing the quality of the building part and its mechanical properties. Most of the researchers utilized different ML techniques to optimize process parameters [44; 45; 46]. The developed model in this work shows that the ML can be utilized as an optimization prediction tool in predicting the density of the building part. [PERSON] et al. [3] developed a Gaussian model to optimize the porosity for the PBF of stainless steel. Further, [PERSON] et al. [26] used a gaussian-based regression analysis to predict the density of AISi10 Mg for the PBF process. [PERSON] et al.