Nowadays there is a strong demand for lighter vehicles in order to increase the pay load. Through this the specific fuel consumption is decreased, the amount of greenhouse gases is lowered and the transport economy improved. One possibility to optimize the weight is to make the components from high strength steels and join them by welding. Welding is the main joining method for fabrication of a large proportion of all engineering structures. Many components experience fatigue loading during all or part of their life time and welded connections are often the prime location of fatigue failure.Fatigue fracture in welded structures often initiates at the weld toe as aconsequence of large residual stresses and changes in geometry acting as stress concentrators. The objective of this research is to increase the understanding of the factors that control fatigue life in welded components made from very high strength steels with a yield strength of more than 800 MPa. In particular the influences of the local weld toe geometry (weld toe radius and angle) and residual stress on fatigue life have been studied. Residual stresses have been varied by welding with conventional as well as Low Transformation Temperature (LTT) filler materials. The three non-destructive techniques Weld Impression Analysis (WIA), Laser Scanning Profiling (LSP) and Structured Light Projection (SLP) have been applied to evaluate the weld toe geometry.Results suggest that all three methods could be used successfully to measure the weld toe radius and angle, but the obtained data are dependent on the evaluation procedure. WIA seems to be a suitable and economical choice when the aim is just finding the radius. However, SLP is a good method to fast obtain a threedimensional image of the weld profile, which also makes it more suitable for quality control in production. It was also found that the use of LTTconsumables increased fatigue life and that residual stress has a relatively larger influence than the weld toe geometry on fatigue strength of welded parts.

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.