Mechanical and Mechatronics Engineering
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This is the collection for the University of Waterloo's Department of Mechanical and Mechatronics Engineering.
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Browsing Mechanical and Mechatronics Engineering by Subject "3D printing"
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Item Additive Manufacturing and Combustion of Graphene Based Nanothermite Aerogels(University of Waterloo, 2024-04-23)Nanothermites are a key material for engineering applications related to energy and heat release, such as welding, heating, and pyrotechnics. The ability to additively manufacture energetic nanothermite structures with desired combustion properties is severely limited by current technology. The reactions are often slow to propagate and release less energy than desired due to polymer binders that act as an energy sink for the reaction but are required to support the energetic material. Additionally, the controllability and tuning of nanothermite reactions are limited by the lack of a conclusive understanding of the reaction mechanisms that govern the reactions of these materials. Several different reaction mechanisms have been proposed without any conclusive evidence to support the claims. In this work we fabricate a polymer-free, reduced graphene oxide (rGO)-based nanothermite aerogel with a range of nanoparticle loadings using a novel additive manufacturing process. The nanothermites analyzed in this work are aluminum iron-oxide (Al/Fe2O3), aluminum copper-oxide (Al/CuO), and aluminum bismuth-oxide (Al/Bi2O3). SEM images demonstrate the unique porous structure formed by the thin rGO sheets which form a skeletal structure that wraps the nanoparticles into individual nanothermite clusters. EDS confirms the homogeneity of the overall structure of the aerogel and the nanothermite clusters, demonstrating that the particles are well dispersed when printing. DSC-TGA results and high-speed combustion videos confirmed the enhanced energetic performance of the printed specimen, suggesting the important role of rGO compared to conventional printing methods. A relatively high linear burn rate of 5.8m/s was demonstrated for rGO/Al/metal oxide samples with a diameter of 1.6mm at 95% nanothermite loading by mass. It was also shown that the propagation rate of the reaction was independent of the print direction. Thermal camera footage clearly indicated the generation of the pre-heating zone, reaction front, and cooling zone during the propagation. SEM, EDS, and TEM are used to analyze the post-combustion material, whose collection is enabled by the novel graphene structure of the material. By analysing the combustion product within the skeletal fragments of the sample the reaction mechanisms for three nanothermite pairings are found and discussed. It is found that in the small nanothermite clusters several previously proposed reaction mechanisms occur concurrently. Diffusion and melt dispersion, which appear to be mutually exclusive in literature, are shown to be occurring concurrently. Overall, this method allows for complex 3D printing fabrication of various rGO/nanoparticle aerogels, while giving insights as to how the reactions propagate via a series of reaction mechanisms depending on the metal oxide used for the reaction.Item In-Situ Monitoring and Quality Assurance Algorithms for Laser Powder-Bed Fusion using Optical Tomography(University of Waterloo, 2024-08-13)Conventional methods for evaluating the quality of components produced through laser powder-bed fusion additive manufacturing (LPBF) are often costly and resource-intensive. These post-production techniques involve mechanical testing, detailed metallographic examination, and non-destructive methods like X-ray computed tomography (CT) to identify flaws. Recently, there has been a shift towards employing in-situ monitoring systems, such as optical tomography (OT), which capture near-infrared light emissions to detect defects arising during LPBF. This dissertation introduces innovative approaches for defect detection in LPBF, utilizing OT data alongside machine learning techniques. LPBF processes inherently exhibit random behavior, presenting challenges in developing robust defect detection algorithms adaptable to diverse machine setups and process parameters. The proposed model integrates a self-organizing map (SOM), a fuzzy logic scheme, and a tailored U-Net architecture to detect and predict defect probabilities in LPBF-produced parts using in-situ OT analysis. Specifically, the model is designed to identify common flaws such as lack of fusion and keyhole defects. The effectiveness of the approach was validated through a series of experiments. Initially, the influence of process parameter selection on recorded in-situ optical tomography (OT) data was investigated. This was followed by the intentional and random recreation of process defects to simulate the stochastic nature of real-world manufacturing processes and to gain a deeper understanding of defect formation. The developed model was subsequently evaluated on a complex geometry to assess its performance in a practical setting. Validation of the model was done by comparing its predictions against computed tomography (CT) scans, to achieve this, Dynamic Time Warping (DTW) technique was used to measure the similarity between porosity curves generated by the model and those from CT scans. The developed model effectively predicted porosity resulting from lack of fusion or keyhole defects across various process parameter settings, achieving average Euclidean distance scores of 0.243 for lack of fusion pores and 0.6 for keyhole pores. Additionally, the model successfully detected defects in complex geometries with internal lattice structures. A significant advantage of this developed model is its adaptability. Fuzzy logic allows for the integration of soft decision boundaries and expert rules into the model, which is crucial when dealing with complex phenomena like porosity where the boundaries between the presence of defects in the fabricated part, based on monitoring OT data, are not always clear-cut. Expert knowledge can be encoded into fuzzy rules that mimic human reasoning and decision-making processes. Quality assurance experts can use their experience to provide insights through the application of fuzzy rules, determining how certain visual or measurable features of an image typically correspond to specific types of porosity. They can also adjust the probability threshold for defect detection based on specific quality criteria. This adaptability enhances the approach's utility across diverse manufacturing scenarios, offering flexibility in meeting quality assurance requirements.Item Melting modes in laser powder bed fusion additive manufacturing(University of Waterloo, 2021-08-18)Laser powder bed fusion (LPBF) is a metal AM technology that has one of the highest industrial uptake at the moment in the aerospace, automotive, and biomedical sectors. LPBF enjoys such popularity as it enables the manufacturing of near-net-shape geometrically complex metal parts. LPBF allows for optimised designs to be explored for manufacturing, such as topology optimised or loading field-driven designs for product lightweighting and customization, while also reducing environmental impact through energy reduction and low carbon dioxide emissions, helping the transition towards sustainable manufacturing. The manufacturing of products using LPBF is almost entirely digitally controlled, from a computer-aided design model to layer-by-layer customization of process parameters, to monitoring and controlling the process while parts are being manufactured. The digitization of metal AM opens up exciting new avenues in areas of design, process planning, process monitoring, and process control. This dissertation focuses primarily on process planning for LPBF. Process planning involves developing a theoretical understanding of the effects of the numerous process parameters that could be digitally controlled in LPBF on the final product outcomes. Within LPBF process planning, it is highly challenging to understand and model the complex laser-material interaction phenomena in LPBF, often resulting in marginally stable process parameters or resulting in a high number of experiments in the development of process parameters required to meet part quality metrics. This dissertation focuses on process physics modelling and simulation at the mesoscale to develop a theoretical understanding of the impact of LPBF process parameters on outcomes such as porous defects, surface topography, and residual stresses. For this purpose, normalized processing diagrams have been developed to visualize the three melting modes (conduction, transition, and keyhole mode) observed in LPBF. The normalized processing diagrams obtained in this dissertation, for the first time in LPBF, are shown to be independent of material, LPBF system, and processing parameters such as powder layer thickness within the datasets presented herein. Additionally, a temperature prediction model has been developed to predict the thresholds between the conduction, transition, and keyhole melting modes. The efficacy of these predicted thresholds has been evaluated experimentally for low reflectivity (titanium and ferrous) alloys and high reflectivity (aluminium) alloys. For low reflectivity alloys, a vaporisation depth greater than 0.5 and 0.8 times the beam spot radius corresponds to the thresholds between conduction to transition mode and transition to keyhole mode respectively. For high reflectivity alloys, surface vaporisation and a vaporisation depth greater than 0.5 times the beam spot radius used corresponds to the thresholds between conduction to transition mode and transition to keyhole mode respectively. Simulations using the normalized processing diagrams and the temperature prediction model are then used to develop a fundamental understanding of porous defects, surface topography, and residual stresses during LPBF of an aluminium alloy (AlSi10Mg) and two titanium alloys (Ti-6Al-4V and Ti-6242Si). For high reflectivity materials such as aluminium alloys, when considering density optimization, divergent beams with resulting focal diameters >100 μm help to obtain a conduction mode microstructure leading to parts with densities of over 99.98%. When working with a focused beam, stabilizing melt pool and spatter dynamics in the transition melting mode by using an appropriate laser power and velocity combination can help in minimizing defects and obtaining densities close to 99.98%, similar to conduction mode densities, albeit with a narrower process parameter window for success. Additionally, a melt pool aspect ratio (ratio of depth to width) of ≈0.4 is observed to be the threshold between conduction and transition/keyhole mode melt pools, which differs from the conventionally assumed melt pool aspect ratio of 0.5. This dissertation thereby provides a novel method to obtain high-quality aluminium alloy parts with defocused and focused beams in LPBF. Such findings can be expanded to other high reflectivity alloys for LPBF. For low reflectivity alloys, when considering density optimization during LPBF of Ti-6242Si, the use of processing diagrams alongside X-ray computer tomography and imaging show that Ti-6242Si has a broad process window with parts above 99.90% density observed in conduction, transition, and keyhole melting modes of LPBF. While the highest density parts (up to 99.98%) are observed in the transition melting mode for Ti-6242Si, transition and keyhole mode LPBF of Ti-6242Si could also lead to macroscopic cracking perpendicular to the build direction, which is attributed primarily to the higher residual stresses during solidification. Furthermore, when considering surface topography, a combination of statistical approaches, simulations, and experiments show that LPBF processing parameters that lie in the keyhole melting mode with lower beam velocity settings and conservative laser powers lead to surface roughness, Sa, values of lesser than 10 μm, which is significantly lower the roughness values obtained for conduction and transition mode LPBF process parameters for Ti-6Al-4V and Ti-6242Si. This significant reduction in surface roughness is due to a negligible contribution from partially melted powder particles in the keyhole melting mode border. Lastly, the fundamental understanding of LPBF developed in this dissertation was leveraged towards biomedical, military, and defence applications. The North American industry has shown a cautious approach to the adoption of LPBF, due to high initial investment costs, the iterative R&D nature of part production, and emerging certification needs. Successful industry adoption of metal additive manufacturing relies on understanding the complex interactions between design, materials, and process to ensure high product quality and reliability. This dissertation would help lower the risk of LPBF technology adoption by virtue of offering a better understanding of the physics behind the laser-material interaction in the process and reducing the need for extensive empirical approaches toward part quality-driven process parameter development.Item On the measurement of relative powder-bed compaction density in powder-bed additive manufacturing processes(Elsevier, 2018-10-05)Experimental studies in the literature have identified the powder-bed compaction density as an important parameter, governing the quality of additively manufactured parts. For example, in laser powder-bed fusion (LPBF), the powder-bed compaction density directly affects the effective powder thermal conductivity and consequently the temperature distribution in melt pool. In this study, this physical parameter in a LPBF build compartment is measured using a new methodology. A UV curable polymer is used to bind powder-bed particles at various locations on the powder-bed compartment when Hastelloy X was used. The samples are then scanned using a nano-computing tomography (CT) system at high resolution to obtain an estimation of the relative powder-bed compaction density. It is concluded that due to the interaction between the recoater and the variation in the powder volume accumulated ahead of the recoater across the build compartment, the relative powder-bed compaction density decreases along the recoater moving direction (from 66.4% to 52.4%.). This variation in the powder-bed compaction density affects the density and surface roughness of the final printed parts that is also investigated. Results show that the part density and surface quality decrease ~0.25% and ~20%, respectively, along the build bed in direction of the recoater motion.Item Towards Predicting Densification and Deformation in the Sintering of Binder-Jet Additively Manufactured Parts(University of Waterloo, 2023-04-10)Binder-jet additive manufacturing (BJAM) is a three-dimensional (3D) printing process which produces parts from successive layers of a powder material (typically metal or ceramic) and selective jetting of a liquid binder to join particles together in each layer. These parts are subsequently exposed to heat treatment steps to remove the binder (de-binding stage) and fuse the particles together (sintering stage) into a final part. During this sintering process, the part experiences shrinkage as the voids between powder particles are eliminated, and can experience distortion due to softening at high temperatures close to the melting point of the material. In this thesis, a modified version of the Skorohod-Olevsky viscous sintering (SOVS) model is presented to model the densification and deformation of samples printed from gas-atomized 4340 low-alloy steel during solid-phase sintering. First, a lumped form of the model is considered for modeling the densification of samples inside a push-rod dilatometer, and trained on one of the data sets. The fitting of the model to the experimental data is done using a derivative-free global optimization approach -- the data-based online nonlinear extremumseeker (DONE) algorithm. The resulting optimized model obtains density prediction errors of at most 3% on the training data, but expectedly experiences greater errors when applied to different heating rates. The modified SOVS model is then implemented in 3D within COMSOL Multiphysics software, and used to predict the densification and deformation of printed 4340 artifacts. The artifacts were sintered inside an optical dilatometer furnace, and the contour data extracted from these experiments was used to train and validate the 3D sintering model using the same optimization approach. The resulting optimized model could predict contour errors within 0.3mm on the training data, and 1.4mm and 0.7mm for validation samples, all with a characteristic length of 20mm. The results show good contour prediction performance, despite the relatively simple nature of the modified SOVS model used in this work, and establishes a basis for further sintering modeling using in-situ thermo-optical measurements.