Additive Manufacturing (AM) is on the verge of causing a downfall to conventional manufacturing with its huge potential in part manufacture. With an increase in demand for customized product, on-demand production and sustainable manufacturing, AM is gaining a great deal of attention from different industries in recent years. AM is redefining product design by revolutionizing how products are made. AM is extensively utilized in automotive, aerospace, medical and dental applications for its ability to produce intricate and lightweight structures. Despite their popularity, AM has not fully replaced traditional methods with one of the many reasons being inferior surface quality. Surface texture plays a crucial role in the functionality of a component and can cause serious problems to the manufactured parts if left untreated. Therefore, it is necessary to fully understand the surface behavior concerning the factors affecting it to establish control over the surface quality.

The challenge with AM is that it generates surfaces that are different compared to conventional manufacturing techniques and varies with respect to different materials, geometries and process parameters. Therefore, AM surfaces often require novel characterization approaches to fully explain the manufacturing process. Most of the previously published work has been broadly based on two-dimensional parametric measurements. Some researchers have already addressed the AM surfaces with areal surface texture parameters but mostly used average parameters for characterization which is still distant from a full surface and functional interpretation. There has been a continual effort in improving the characterization of AM surfaces using different methods and one such effort is presented in this thesis.

The primary focus of this thesis is to get a better understanding of AM surfaces to facilitate process control and optimization. For this purpose, the surface texture of Fused Deposition Modeling (FDM) and Laser-based Powder Bed Fusion of Metals (PBF-LB/M) have been characterized using various tools such as Power Spectral Density (PSD), Scale-sensitive fractal analysis based on area-scale relations, feature-based characterization and quantitative characterization by both profile and areal surface texture parameters. A methodology was developed using a Linear multiple regression and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces and also to understand the manufacturing process. The results suggest that the developed approaches can be used as a guideline for AM users who are looking to optimize the process for gaining better surface quality and component functionality, as it works effectively in finding the significant parameters representing the unique signatures of the manufacturing process. Future work involves improving the accuracy of the results by implementing improved statistical models and testing other characterization methods to enhance the quality and function of the parts produced by the AM process.