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DTSTART;TZID=America/New_York:20210706T103000
DTEND;TZID=America/New_York:20210706T120000
DTSTAMP:20260406T205525
CREATED:20210625T162928Z
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SUMMARY:MEAM Seminar: "Nanocellulose Fibers as Reinforcement to Improve Strength and Toughness in Structural Materials"
DESCRIPTION:Cellulose nanofibrils (CNFs) are a naturally abundant polymer and have superior mechanical properties\, high aspect ratios\, are transparent and biodegradable\, which make them attractive to be used in structural materials. As a reinforcing phase\, CNFs have the potential to improve the mechanical properties of polymer materials. While there are active research efforts aimed at incorporating CNFs into polymers for use as structural materials\, the field is still in its infancy due to the challenge of achieving good compatibility and the challenge of maintaining strength while trying to enhance fracture toughness and crack growth resistance. \nIn the first part of this study\, one dimensional composite fibers are fabricated using TEMPO-CNF to increase the strength and toughness of the common polymer PMMA\, a hydrophobic and challenging material to bond with CNFs. The composite fibers were prepared via solvent exchange\, melt-spinning and drawing to obtain fibers of diameters around 200 microns. Tensile testing\, image correlation to measure the strains\, and fracture test with flat faced edge crack demonstrated the enhancement of modulus\, strength\, and fracture toughness through the addition of CNFs to PMMA. Specifically\, an enhancement of 35% in Modulus\, 19% in Strength and 100% in fracture toughness were observed at 1% by wt. CNF content. \nThe second part is a distinct but interrelated research thrust to the traditional polymer integration. Composite pure cellulose sheets consisting of micro- and nanocellulose are fabricated. This study uses printing and subsequent drying processes to infuse nanocellulose into the paper matrix in various patterns to increase the strength and toughness of the network. Tensile tests and single edge notch tension (SENT) tests are performed on the specimens to evaluate their tensile and fracture behavior. Linear elastic finite element modeling is used to help guide the experimental work. This work has potential applications in using nanocellulose fibers to realize fully degradable alternatives to thin plastic sheets.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-nanocellulose-fibers-as-reinforcement-to-improve-strength-and-toughness-in-structural-materials/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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DTSTART;TZID=America/New_York:20210709T090000
DTEND;TZID=America/New_York:20210709T100000
DTSTAMP:20260406T205525
CREATED:20210706T160206Z
LAST-MODIFIED:20210706T160206Z
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SUMMARY:ESE PhD Dissertation Defense: "Balancing Fit and Complexity in Learned Representations"
DESCRIPTION:Thesis Title: Balancing Fit and Complexity in Learned Representations \nAbstract: This dissertation is about learning representations of functions while restricting complexity. In machine learning\, maximizing the fit and minimizing the complexity are two conflicting objectives. Common approaches to this problem involve solving a regularized empirical minimization problem\, with a complexity measure regularizer and a regularizing parameter that controls the trade-off between the two objectives. The regularizing parameter has to be tuned by repeatedly solving the problem and does not have a straightforward interpretation. This work formulates the problem as a minimization of the complexity measure subject to the fit constraints. \nThe issue of complexity is tackled in reproducing kernel Hilbert spaces (RKHSs) by introducing a novel integral representation of a family of RKHSs that allows arbitrarily placed kernels of different widths. The functional estimation problem is then written as a sparse functional problem\, which despite being non-convex and infinite-dimensional can be solved in the dual domain. This problem achieves representations of lower complexity than traditional methods because it searches over a family of RKHS rather than a subspace of a single RKHS. \nThe integral representation is used in a federated classification setting\, in which a global model is trained from a federation of agents. This is possible due to the observation that the dual optimal variables give information about the samples which are fundamental to the classification. Each agent\, therefore\, learns a local model and sends only the fundamental samples over the network. This creates a federated learning method that requires only one network communication. Its solution is proven to asymptotically converges to that of traditional classification. \nNext\, a theory for constraint specification is established. An optimization problem with a constraint for each sample point can easily become infeasible if the constraints are too tight. In contrast\, relaxing all constraints can cause the solution to not fit the data well. The constrained specification method relaxes the constraints until the marginal cost of changing a constraint is equal to the marginal complexity measure. This problem is proven to be feasible and solvable\, and shown empirically to be resilient to outliers and corrupted training data. \nFor Zoom link\, please email Elizabeth Kopeczky at: kopeczky@seas.upenn.edu.
URL:https://seasevents.nmsdev7.com/event/phd-dissertation-defense-maria-peifer/
LOCATION:PA
CATEGORIES:Dissertation or Thesis Defense
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210709T130000
DTEND;TZID=America/New_York:20210709T140000
DTSTAMP:20260406T205525
CREATED:20210629T200051Z
LAST-MODIFIED:20210629T200051Z
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SUMMARY:MEAM MSE Thesis Defense: "Design and Characterization of an Origami-Inspired Robot that Swims via Jet Propulsion"
DESCRIPTION:Underwater swimmers present unique opportunities for using bodily reconfiguration for self-propulsion. Origami-inspired designs are low-cost\, fast to fabricate\, robust\, and can be used to create compliant mechanisms useful in energy-efficient underwater locomotion. This thesis demonstrates an origami-inspired robot that can change its body shape to ingest and expel water\, creating a jet that propels it forward similarly to cephalopods. We use the magic ball origami pattern\, which can transform between ellipsoidal (low volume) and spherical (high volume) shapes. We modified the pattern by tuning the number of its rows and columns and reported their stiffness and mechanical properties. A custom actuation mechanism contracts the robot to take in fluid\, and the inherent mechanics of the magic ball returns the robot to its natural shape upon release. We describe the design and control of this robot and verify its locomotion in a water tank. The resulting robot is able to move forward at 6.7 cm/s (0.2 body lengths/s)\, with a cost of transport of 2.0. The discussion in this thesis is mainly based on the first-generation prototype of the robot\, a future design plan to improve the robot is explained as well.
URL:https://seasevents.nmsdev7.com/event/meam-mse-thesis-defense-design-and-characterization-of-an-origami-inspired-robot-that-swims-via-jet-propulsion/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Dissertation or Thesis Defense,Master's
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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