# Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques
## 1 Introduction  
The unique capabilities of the additive manufacturing (AM) process such as complexity-free, customization and functionally gradient part properties, offers a broad application of AM in industries [1]. Specifically, AM is advantageous for developing complex turbine blades, customized bio-implants, and internal cooling channels [2]. As the AM is growing in the industry, process establishment for new materials with better processibility, functionality, and desired mechanical properties are challenging task that needs to be explored. In the powder bed fusion additive manufacturing (PBF-AM) process, various parameters are involved starting from the laser system parameter, build chamber parameter, design parameter, and machine parameters, making process establishment more complex, as shown in Figure 1[3]. The conventional process optimization techniques such as the trial-and-error approach, the finite element method, the design of experiments, numerical and mathematical modeling, and physics-based simulation are used to stabilize the newly developed process [4]. These conventional techniques need expertise in material science as well as knowledge in the mechanical domain. Additionally, these techniques work under certain assumptions and require more time and high computational power [5]. Machine learning (ML) provides an advanced predictiontool developed under artificial intelligence's umbrella [6]. ML is the technique that makes the machine learn from past data to achieve prediction with maximum accuracy [7]. ML techniques for additive manufacturing have become a focus area for researchers to benefit from the process [8; 9; 10; 11; 12]. The motivation of this work is to combine machine learning techniques to achieve a dense structure in powder bed fusion AM. The present study explores the application of ML techniques such as ANN, KNN, SVM, and linear regression to predict the density of additively manufactured parts. ANN technique utilizes a principle similar to human intelligence [13]. Further, it establishes the relationship between input and output parameters using past experience and trends. It contains numerous neurons and hidden layers associated with parameter complexity [14]. K-nearest neighbor (KNN) is used for classification and regression analysis and does not store any learning outcome from the training dataset [15]. Support vector machine (SVM) is one of the most promising supervised machine learning algorithms applied for regression and classification problems [16]. The linear regression model is simple to perform and easy to interpret the output analysis. However, linear regression assumes a linear relationship between the parameters. Linear regression also looks at a relationship between the mean of dependent variables and independent variables. Linear regression is not a complete description of relationships among variables. The data-driven ML model was developed in this work to predict the density of SS316L built by powder-based AM. SS316L has extensive applications in the bio-implant where the complexity and mechanical property must be precisely taken care [17]. The density is directly related to all other mechanical properties. The higher density would improve tensile strength, hardness, and other mechanical properties [18]. Controlling density is crucial in the powder-based AM due to several parameters at deposition [19]. The layer-wise deposition in AM is prone to more porous parts than in other manufacturing processes. Hence, density optimization is significant in measuring the quality of the AM-built part. The selection of process parameters concerning optimum density will help predict the quality of the part and the mechanical properties. The data used in the present work is collected from previous studies based on the powder-based AM of SS316L, as shown in Table 1[17; 20; 21; 22; 23; 24]. The data was first classified and filtered according to input and output parameters.