Discovery Learning Experiments in a New Machine Design Laboratory

被引:0
|
作者
Nagurka, Mark [1 ,2 ]
Anton, Fernando Rodriguez [3 ]
机构
[1] Marquette Univ, Mech & Biomed Engn, Milwaukee, WI 53233 USA
[2] Marquette Univ, Engn Pedag, Milwaukee, WI 53233 USA
[3] Marquette Univ, Mech Engn, Milwaukee, WI 53233 USA
关键词
D O I
暂无
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
A new Machine Design Laboratory at Marquette University has been created to foster student exploration with hardware and real-world systems. The Laboratory incorporates areas for teaching and training, and has been designed to promote "hands-on" and "minds-on" learning. It reflects the spirit of transformational learning that is a theme in the College of Engineering. The goal was to create discovery learning oriented experiments for a required junior-level "Design of Machine Elements" course in mechanical engineering that would give students practical experiences and expose them to physical hardware, actual tools, and real-world design challenges. In the experiments students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory and spark interest in the subject of machine design. This paper presents details of the experiments and summarizes student reactions and our experiences in the Machine Design Laboratory. In addition, the paper provides some insights for others who may wish to develop similar types of experiments.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design
    Markus, Braun
    Christian, Gruber C.
    Andreas, Krassnigg
    Arkadij, Kummer
    Stefan, Lutz
    Gustav, Oberdorfer
    Elina, Siirola
    Radka, Snajdrova
    [J]. ACS CATALYSIS, 2023, 13 (21) : 14454 - 14469
  • [42] A new design for honey bee hoarding cages for laboratory experiments
    Koehler, Angela
    Nicolson, Susan W.
    Pirk, Christian W. W.
    [J]. JOURNAL OF APICULTURAL RESEARCH, 2013, 52 (02)
  • [43] Machine-learning guided discovery of a new thermoelectric material
    Yuma Iwasaki
    Ichiro Takeuchi
    Valentin Stanev
    Aaron Gilad Kusne
    Masahiko Ishida
    Akihiro Kirihara
    Kazuki Ihara
    Ryohto Sawada
    Koichi Terashima
    Hiroko Someya
    Ken-ichi Uchida
    Eiji Saitoh
    Shinichi Yorozu
    [J]. Scientific Reports, 9
  • [44] Machine-learning guided discovery of a new thermoelectric material
    Iwasaki, Yuma
    Takeuchi, Ichiro
    Stanev, Valentin
    Kusne, Aaron Gilad
    Ishida, Masahiko
    Kirihara, Akihiro
    Ihara, Kazuki
    Sawada, Ryohto
    Terashima, Koichi
    Someya, Hiroko
    Uchida, Ken-ichi
    Saitoh, Eiji
    Yorozu, Shinichi
    [J]. SCIENTIFIC REPORTS, 2019, 9 (1)
  • [45] Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery
    Giguere, Sebastien
    Laviolette, Francois
    Marchand, Mario
    Tremblay, Denise
    Moineau, Sylvain
    Liang, Xinxia
    Biron, Eric
    Corbeil, Jacques
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (04)
  • [46] Advances in the application of machine learning techniques in drug discovery, design and development
    Barrett, S. J.
    Langdon, W. B.
    [J]. APPLICATIONS OF SOFT COMPUTING: RECENT TRENDS, 2006, : 99 - +
  • [47] Machine learning improves metal-organic frameworks design and discovery
    Tamakloe, Senam
    [J]. MRS BULLETIN, 2022, 47 (09) : 886 - 886
  • [48] Machine learning for colloidal crystal structure and property discovery & inverse design
    Glotzer, Sharon
    [J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 256
  • [49] Active learning machine learns to create new quantum experiments
    Melnikov, Alexey A.
    Nautrup, Hendrik Poulsen
    Krenn, Mario
    Dunjko, Vedran
    Tiersch, Markus
    Zeilinger, Anton
    Briegel, Hans J.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2018, 115 (06) : 1221 - 1226
  • [50] Exploring new useful phosphors by combining experiments with machine learning
    Takeda, Takashi
    Koyama, Yukinori
    Ikeno, Hidekazu
    Matsuishi, Satoru
    Hirosaki, Naoto
    [J]. Science and Technology of Advanced Materials, 2024, 25 (01)