skip to main content

Search for: All records

Award ID contains: 1946231

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    Our mother nature has been providing human beings with numerous resources to inspire from, in building a finer life. Particularly in structural design, plenteous notions are being drawn from nature in enhancing the structural capacity as well as the appearance of the structures. Here plant stems, roots and various other structures available in nature that exhibit better buckling resistance are mimicked and modeled by finite element analysis to create a training database. The finite element analysis is validated by uniaxial compression to buckling of 3D printed biomimetic rods using a polymeric ink. After feature identification, forward design and data filtering are conducted by machine learning to optimize the biomimetic rods. The results show that the machine learning designed rods have 150% better buckling resistance than all the rods in the training database, i.e., better than the nature’s counterparts. It is expected that this study opens up a new opportunity to design engineering rods or columns with superior buckling resistance such as in bridges, buildings, and truss structures.

  2. Free, publicly-accessible full text available June 1, 2023
  3. Free, publicly-accessible full text available March 1, 2023
  4. Free, publicly-accessible full text available February 11, 2023
  5. Currently, no commercial aluminum 7000 series filaments are available for making aluminum parts using fused deposition modeling (FDM)-based additive manufacturing (AM). The key technical challenge associated with the FDM of aluminum alloy parts is consolidating the loosely packed alloy powders in the brown-body, separated by thin layers of surface oxides and polymer binders, into a dense structure. Classical pressing and sintering-based powder metallurgy (P/M) technologies are employed in this study to assist the development of FDM processing strategies for making strong Al7075 AM parts. Relevant FDM processing strategies, including green-body/brown-body formation and the sintering processes, are examined. The microstructures of the P/M-prepared, FDM-like Al7075 specimens are analyzed and compared with commercially available FDM 17-4 steel specimens. We explored the polymer removal and sintering strategies to minimize the pores of FDM-Al7075-sintered parts. Furthermore, the mechanisms that govern the sintering process are discussed.
    Free, publicly-accessible full text available February 1, 2023
  6. Free, publicly-accessible full text available January 1, 2023
  7. Classification is one of the fundamental tasks in machine learning. The quality of data is important in con- structing any machine learning model with good prediction performance. Real-world data often suffer from noise which is usually referred to as errors, irregularities, and corruptions in a dataset. However, we have no control over the quality of data used in classification tasks. The presence of noise in a dataset poses three major negative consequences, viz. (i) a decrease in the classification accuracy (ii) an increase in the complexity of the induced classifier (iii) an increase in the training time. Therefore, it is important to systematically explore the effects of noise in classification performance. Even though there have been published studies on the effect of noise either for some particular learner or for some particular noise type, there is a lack of study where the impact of different noise on different learners has been investigated. In this work, we focus on both scenar- ios: various learners and various noise types and provide a detailed analysis of their effects on the prediction performance. We use five different classifiers (J48, Naive Bayes, Support Vector Machine, k-Nearest Neigh- bor, Random Forest) and 10 benchmark datasets frommore »the UCI machine learning repository and three publicly available image datasets. Our results can be used to guide the development of noise handling mechanisms.« less
    Free, publicly-accessible full text available January 1, 2023
  8. Free, publicly-accessible full text available January 1, 2023
  9. Free, publicly-accessible full text available December 22, 2022
  10. Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35%more »higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.« less
    Free, publicly-accessible full text available December 1, 2022