Abstract Not Provided. / Thesis / Bachelor of Science (BSc)

This thesis focuses on a measuring and topography of a components´ surface evaluation and it contains the overview of basic methods which includes the description of contact and non-contact measuring devices. Furthermore, the thesis also describes ways of surface texture evaluation through profiled and areal method, including an outline of ČSN ISO parameters. The practical part of the thesis focuses on the evaluation of metric threads´ M10 surface, crafted by diverse cutting and forming threads. The surface was scanned with the optical device called Alicona IFM G5 and it was evaluated through the profiled and areal method. The 2D and 3D parameters were acquired and then compared during the measuring.

The main theme of this thesis is the surface topography evaluation. The theoretical part introduces the fundamental methods of evaluation, measuring instruments and describes profile and surface topographic parameters. In the practical part, the influence of different cutting conditions in high-feed face milling was researched on sample made of aluminium alloy 7475-T7351. For the surface topography evaluation of all samples the measuring device Alicona IF G5 was used. The last part of the diploma thesis is devoted to the economic influence of cutting conditions on the process of high-feed face milling.

The ease of manufacturing complex geometries using fused deposition modeling (FDM) 3D-Printing reduces the overall production cost compared with the traditional manufacturing techniques. Because of the benefits of 3D printing technologies, it is proposed to be used in the manufacturing of different products. But there is still no definite characterization of the surface quality of objects manufactured by 3D printing. Hence in order to define the texture of the surfaces produced, measurements from different samples are taken and quantified.In this study, a 3D test model consisting of various slopes is printed at different layer thicknesses and different print speeds using different 3D printers.Thus, the effect of the surface orientation on the surface roughness was studied in relation to the different layer thicknesses and different print speeds. The study samples are measured using the state of the art equipment at Halmstad University.This thesis studies the surface roughness at different slopes of FDM models.A related study on the dimensional variation between the CAD model and the actual3D printed model, and causes/reasons for the variations are analyzed.It is observed that FDM produced part surface topography is directly affected by the orientation of the surface. Also, the surface roughness increases with increase in layer thickness. The observed correlations between surface roughness and layer thickness and surface orientation could be used to better understand the behavior of FDM surfaces, thus to better quantify the surface roughness. To improve quality, it must first be quantified. It is well observed that dimensional inaccuracy exists between the CAD model and the printed part. These results suggest that there is a lot of work and improvements to be done in order to close the gap of dimensional inaccuracy and achieve a high precision commercial FDM 3Dprinting.

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.