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FDM 3D printing is an additive manufacturing technology that is widely used, mainly for rapid prototyping. It is also one of the cheapest and most accessible AM technologies for consumers. FDM printers, and especially cheaper alternatives, can have problems with creating high quality prints. Reasons include poor design, inaccurate construction, cheap components, and improper tuning. Input shaping is a control technique that may help mitigate defects caused by poor mechanical design or construction. The “ringing” defect may be eliminated by applying this solution. To perform an evaluation in sub-optimal mechanical conditions a 3D printer was constructed with a cantilever design mainly using plastic prints for mechanically important parts. Printing tests were done with and without input shaping. The results that were produced showed a direct effect of input shaping in 3d printers. / FDM 3D-printing är en additiv tillverkningsteknik som är mycket använd, främst för snabb prototypering. Det är också en av de billigaste och mest tillgängliga AM-teknikerna för konsumenter. FDM skrivare, och särskilt billigare alternativ, kan ha problem med att skapa högkvalitativa utskrifter. Orsaker inkluderar dålig design, konstruktionfel, billiga komponenter och felaktig justering. Input shaping är en kontrollteknik som kan hjälpa till att mildra defekter som orsakas av dålig mekanisk design eller konstruktion. "Ringning" defekten kan elimineras genom att tillämpa denna lösning. För att utföra en utvärdering i dåliga mekaniska förhållanden konstruerades en 3D-skrivare med en fribärande design som använder plastutskrifter för mekaniskt viktiga delar. Utskriftstester gjordes med och utan input shaping. Resultaten som framställdes visade på en uppenbar förbättring av print kvalité som en direkt effekt av input shaping.

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In this study, we quantified the Chemistry-Process-Structure-Property (CPSP) relations of a Ti-6Al-4V/TiB functionally graded material to assess its ability to withstand large deformations in a high throughput manner. The functionally graded Ti-6Al-4V/TiB alloy was created by using a Laser Engineered Net Shaping (LENS) process. A complex thermal history arose during the LENS process and thus induced a multiscale hierarchy of structures that in turn affected the mechanical properties. Here, we quantified the functionally graded chemical composition; functionally graded TiB particle size, number density, nearest neighbor distance, and particle fraction; grain size gradient; porosity gradient. In concert with these multiscale structures, we quantified the associated functionally graded elastic moduli and overall stress-strain behavior of eight materials with differing amounts of titanium, vanadium, aluminum, and boron with just one experiment under compression using digital image correlation techniques. We then corroborated our experimental stress behavior with independent hardening experiments. This paper joins not only the Process-Structure-Property (PSP) relations, but couples the different chemistries in an efficient manner to effectively create the CPSP relationships for analyzing titanium, aluminum, vanadium, and boron together. Since this methodology admits the CPSP coupling, the development of new alloys can be solved by using an inverse method. Finally, this experimental data now lays down the gauntlet for modeling the sequential CPSP relationships.

Additive Manufacturing (AM) has revolutionized manufacturing processes by enabling the realization of custom products with intricate geometric features that were either too complex or even intractable for subtractive manufacturing processes. Yet, functional surfaces generated in AM have to be often finish machined because of their relatively inferior roughness. The first phase of this research worked around this limitation by tailoring the topography of an AM surface in-process to entail textures that further enhance certain functionalities in a process called Additive Texturing (AT). In this context, the Selective Laser Melting (SLM) process ability to realize intricate surface microfeatures was explored experimentally, evaluating its geometrical limitations. Utilizing such limitations, various patterns comprising pillars, channels, and re-entrant structures were printed to control the wetting behaviour of SLM stainless steel. AT's efficacy is demonstrated in its capability to generate hydrophobic AM surfaces with water contact angles exceeding 140°. Similarly, other texturing patterns comprising dimples, linear, V-shaped, and X-shaped grooves were investigated to tailor the tribological response of textured surfaces under dry sliding conditions. Evidently, a specific wear rate and coefficient of friction reduction of 80% and 60%, respectively, demonstrated another potential for AT. The undesirable tensile Residual Stresses (RS) that inevitably accumulate during the SLM process's rapid heating and cooling cycles were investigated in the second phase of this research. Laser Peening (LP) was utilized to post-process the printed samples to eliminate the initial tensile RS and induce near 500 Mpa compressive RS. Moreover, the LP parameters were explored and optimized to enhance RS, surface roughness, hardness, and wear resistance. / Thesis / Doctor of Philosophy (PhD)

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Oxide formation on metallic surfaces is a common phenomenon which occursnaturally or intently. Depending on the metallic oxide, they can be viewed as either nuisances or conveniences depending on the effects of the oxide. Formed oxides may also potentially smooth surfaces of metallic alloys since a portion of the surface in contact with the oxygen will be converted into the oxide via the metal-oxygeninteraction, leading to a smoother surface underneath the formed oxide. It was found that oxide formation was most significant when metallic Vibenite® alloys were treated at 1000°C for a minimum of 3 hours with an oxygen flow into the oven of 10 L/min. This signifies the importance of a minimum temperature limit as well as an increased oxygen pressure within the oven the samples are being treated in, which concurs with various studies referred to in the report. The oxides were also somewhat successfully identified using analysis methods such as XPS, XRD and Raman spectroscopy with supporting evidence from simulated Thermo-Calc approximations. Thepost-treatment surfaces of the samples, after having their oxide layers removed, were confirmed to have undergone surface smoothing using the optical analysis method of VSI. The results of this report indicate validity in the use of the oxide formation technique for surface smoothing and strongly suggests further study in material optimised heat-treatments for different metallic alloys with the purpose of surface refinement

This dissertation aims to provide efficient process defect identification methods for advanced manufacturing environments using AI tools/algorithms with limited labeled data availability. Asset and equipment quality become highly sensitive in sustaining virtuous performance and safety in various manufacturing domains. Internally generated process imperfections degrade finished products' optimum performance and mechanical attributes. The evolution of big data and intelligent sensing systems leverage data-driven defect identification in advanced manufacturing environments. Widely adopted data-driven process anomaly detection methods assume that the training (source) and testing (target) data follow the same distribution and that labeled data are available in both source and target domains. However, the source and target sometimes follow different distributions in real-world manufacturing environments as the diversity of industrialization processes leads to heterogeneous data collection under different production conditions. Such a case significantly limits the performance of AI algorithms when distribution discrepancy exists. Moreover, labeling data is typically costly and time-consuming, signifying that identifying process defects is limited by rare labeled data. Also, more realistic industrial applications incorporate fewer defect data than ordinal data and unforeseen target defects, leading to complications in understanding the process behaviors in various aspects. Therefore, we introduced methodological principles, including unsupervised grouping, transfer learning, data augmentation, and ensemble learning to address these limitations in advanced operations. First, rapid porosity prediction methodology for additive manufacturing (AM) processes under varying process conditions is developed by leveraging knowledge transfer from existing process conditions. Second, designing an effective classification method concerning time series signals to advance predictive maintenance (PdM) for machine state prediction is discussed. Finally, a data augmentation-based stacking classifier approach is developed to enhance the precision of predicting porosity, even when limited porosity data is available.

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