Tags: machine learning

Resources (1-13 of 13)

  1. Interval Deep Learning for Uncertainty Quantification in Engineering Problems

    10 Jun 2021 | Contributor(s):: Betancourt, David

    Advisors: Rafi L Muhanna, B. Aditya Prakash, Chao Zhang, Abdul-Hamid Zureick, Steffen Freitag, Vladik KreinovichDeep neural networks are becoming more common in important real-world safety-critical applications where reliability in the predictions is paramount. Despite their exceptional...

  2. Scan Strategy Interpolation for Laser Powder Bed Fusion in Manufacturing Applications

    10 Jun 2021 | Contributor(s):: Jung, Patrick V.

    Advisors: Christopher Saldana, Katherine Fu, Thomas KurfessLaser Powder Bed Fusion (L-PBF) is a technique within additive manufacturing which uses a high power density laser to build parts from fused powdered metal alloy. This technology is well equipped to produce complex parts with otherwise...

  3. Data-Driven Process-Structure-Property Models for Additive Manufactured Ni-Base Superalloys

    11 Jan 2021 | Contributor(s):: GorganNejad, Sanam

    Advisors: Richard Neu, David McDowell, Antonia Antoniou, Joshua Kacher, Kamran PaynabarThe complexity of the selective laser melting (SLM) process, which has shown success for shaping advanced structural alloys, has concentrated most of the research efforts to develop process-structure-property...

  4. The Effectiveness of Various Chatter Detection Methods Under Noisy Conditions

    08 Sep 2020 | Contributor(s):: Lu, Lance C.

    Advisors: Christopher Saldana, Thomas Kurfess, Katherin FuUnmanned operations are sought after in manufacturing processes such as milling and lathing. During these processes, the detection and mitigation of machine tool chatter is critical. The veracity of these methods under noise conditions...

  5. Clustering and Feature Detection Methods for High-Dimensional Data

    08 Sep 2020 | Contributor(s):: Lahoti, Geet

    Advisors: Chuck Zhang, Kamran Paynabar, Jianjun Shi, Ben Wang, Zhen QianThe majority of the real-world data are unlabeled. Moreover, complex characteristics such as high-dimensionality and high variety pose significant analytical challenges. In statistical and machine learning, supervised and...

  6. Using Machine Learning for Anomalous Toolpath Identification in Subtractive Manufacturing

    20 May 2020 | Contributor(s):: Nguyen, Edward Pham

    Advisors: Christopher Saldana, Thomas Kurfess, Katherine FuThe emphasis and application of machine learning with respect to manufacturing and machining has focused primarily on tool wear or bearing health. Few studies have focused on the parts produced by these processes and how changing...

  7. Multiscale Uncertainty Quantification for Physics-Based Data-Driven Materials Design and Optimization

    14 Jan 2020 | Contributor(s):: Tran, Anh Vuong

    Advisors: Yan Wang, David L. McDowell, Chaitanya Deo, Hongyuan Zha, Xin SunUncertainty is a critical element in computational materials science. From the experimental perspective, the sources of uncertainty include measurement errors caused by instrument, operator, and sensing models, as well as...

  8. Flow-Assisted Erosion-Corrosion of Steel in Pulping Liquors Containing Particulates

    31 May 2018 | Contributor(s):: Baykal, Bedi Aydin

    Advisors: Preet M. Singh, Faisal M. Alamgir, Victor Breedveld, Hamid Garmestani, Arun M. GokhaleErosion-Corrosion is a type of corrosion where mechanical (erosive) and chemical (corrosive) effects combine to accelerate material loss due to corrosion. Erosion-corrosion can cause a higher rate of...

  9. Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures: codes and data files

    01 Feb 2018 | Contributor(s):: Noah H. Paulson

    This data-set contains the codes and data files relevant to the paper titled "Data-driven reduced-order models for rank-ordering thehigh cycle fatigue performance of polycrystalline microstructures" [Paulson2017_HomogenizationHCF]. Data-files are provided so that the figures...

  10. Analysis of Fibrillar Structures for the Engineering of Polymeric Transistors

    22 Jan 2018 | Contributor(s):: Persson, Nils Erland

    Advisors: Martha A. Grover, Elsa Reichmanis, Carson Meredith, Joseph Schork, Jye-Chyi LuImage processing software was developed and applied to the analysis of polymer nanofiber microstructures in poly(3-hexylthiophene) (P3HT)-based organic field effect transistors. A large database of processing,...

  11. A Framework for the Optimization of Doctrine and Systems in Army Air Defense Units Using Predictive Models of Stochastic Computer Simulations

    07 Jun 2017 | Contributor(s):: Wade, Brian M.

    Advisors: Daniel Schrage, Dimitri Mavris, Lakshmi Sankar, Apinut Sirirojvisuth, David KnudsonThis thesis presents a new methodology that can be used to address large-scale raids made up of different types of Theater Ballistic Missiles (TBMs) and Cruise Missiles (CMs) that attempt to overwhelm the...

  12. Process-Structure Linkages With Materials Knowledge Systems

    11 Jan 2017 | Contributor(s):: Brough, David

    Advisors: Surya Kalidindi, Srinivas Aluru, Hamid Garmestani, Martha A. Grover, Hongyuan ZhaThe search for optimal manufacturing process routes that results in the combination of desired properties for any application is a highly dimensional optimization problem due to the hierarchical nature of...

  13. A Framework for Understanding and Controlling Batch Cooling Crystallization

    22 Aug 2016 | Contributor(s):: Griffin, Daniel J.

    Advisors: Ronald W. Rousseau, Martha A. Grover, Yoshiaki Kawajiri, Matthew J. Realff, Bojan PetrovicIn taking a different view of crystallization dynamics, this thesis reveals a new framework for addressing a prevalent process engineering challenge: control over the size of crystals produced by...