skip to main content


Title: Multi-scale Characterization and Visualization of Metallic Structures to Improve Solid Mechanics Education
This paper presents the development and preliminary implementation of a multi-scale material and mechanics education module to improve undergraduate solid mechanics education. We experimentally characterize 3D printed and conventional wrought aluminum samples and collect structural images and perform testing at the micro- and macro- scales. At the micro-scale, we focus on the visualization of material’s grain structures. At the macro-scale, standard material characterization following ASTM standards is conducted to obtain the macroscopic behavior. Digital image correlation technology is employed to obtain the two-dimensional strain field during the macro-scale testing. An evaluation of student learning of solid mechanics and materials behavior concepts is carried out to establish as baseline before further interventions are introduced. The established multi-scale mechanics and materials testing dataset will be also used in a broad range of undergraduate courses, such as Solid Mechanics, Design of Mechanical Components, and Manufacturing Processes, to inform curricular improvement. The successful implementation of this multi-scale approach for education is likely to enhance students’ understanding of abstract solid mechanics theories and establish links between mechanics and materials concepts. More broadly, this approach will assist advanced solid mechanics education in undergraduate engineering education throughout the country.  more » « less
Award ID(s):
1712178
NSF-PAR ID:
10125834
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
2019 ASEE Annual Conference & Exposition
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. This paper presents the development and preliminary implementation of a multi-scale material and mechanics education module to improve undergraduate solid mechanics education. We experimentally characterize 3D printed and conventional wrought aluminum samples and collect structural images and perform testing at the micro- and macro- scales. At the micro-scale, we focus on the visualization of material’s grain structures. At the macro-scale, standard material characterization following ASTM standards is conducted to obtain the macroscopic behavior. Digital image correlation technology is employed to obtain the two-dimensional strain field during the macro-scale testing. An evaluation of student learning of solid mechanics and materials behavior concepts is carried out to establish as baseline before further interventions are introduced. The established multi-scale mechanics and materials testing dataset will be also used in a broad range of undergraduate courses, such as Solid Mechanics, Design of Mechanical Components, and Manufacturing Processes, to inform curricular improvement. The successful implementation of this multi-scale approach for education is likely to enhance students’ understanding of abstract solid mechanics theories and establish links between mechanics and materials concepts. More broadly, this approach will assist advanced solid mechanics education in undergraduate engineering education throughout the country. 
    more » « less
  2. null (Ed.)
    Classical mechanics courses are taught to most engineering disciplinary undergraduate students. Due to the recent advancements of multiscale analysis and practice, necessary reforms need to be investigated and explored for classical mechanics courses to address the materials’ mechanics behaviors across multiple length scales. This enhanced understanding is needed for engineering students to consider materials more broadly. This paper presents a recent effort for the development of a multiscale materials and mechanics experimentation (M3E) module that can be potentially implemented in undergraduate mechanics courses, including Statics, Dynamics, Strength of Materials, and Design of Mechanical (Machine) Components. The developed education module introduces the concepts of multiscale materials behavior and microstructures in the form of micro and macro-scales. At the micro-scale, both 3D printed aluminum and cold-rolled aluminum samples were characterized using scanning electron microscope. Microstructures, including grains, grain boundaries, dislocation, precipitates, and micro-voids, were demonstrated to students. At the macro-scale, experiments following ASTM standards were conducted and full strain fields carried by all the samples were analyzed using digital image correlation method. The experimental data were organized and presented to the students in the developed M3E module. The implementation of the developed module in undergraduate mechanics classes allows students to not only visualize materials behavior under various load conditions, but also understand the reasons behind classical mechanics properties. To assess the effectiveness of the developed M3E education module, an evaluation question was developed. Students are required to classify key mechanics, materials, and processing concepts at both micro and macroscales. More than 40 fundamental concepts and keywords are included in the tests. The study outcomes and effectiveness of the M3E education module will be reported in this paper. 
    more » « less
  3. null (Ed.)
    Geotechnical engineering undergraduate curriculum typically consist of courses in soil mechanics and foundation design that include a variety of topics that are difficult for students to understand and master. Behavior of the below grade natural and built geomaterials discussed in these courses can be difficult for students to visualize. Typically, the mechanisms of behavior are demonstrated using small-scale laboratory tests, two-dimensional sketches, simple table-top models, or video simulations in the classroom. Students rarely have the opportunity to observe large-scale behavior of foundations in the field or laboratory. The authors from Rose-Hulman Institute of Technology and Saint Louis University designed and implemented a large-scale foundation testing system to address several topics that students tend to struggle with the most, including 1) the difference in strength and service limit states in shallow foundation design, 2) soil-structure interaction associated with lateral behavior of deep foundations, and 3) the influence of near-surface soil on lateral behavior of deep foundations. This paper provides a detailed overview of the design, fabrication, and implementation of two large-scale experiential learning modules for undergraduate courses in soil mechanics and foundation engineering. The first module utilizes shallow foundations in varying configurations to demonstrate the differences in strength and service limit state behavior of shallow foundations. The second module utilizes a relatively flexible pile foundation embedded in sand to demonstrate the lateral behavior of deep foundations. The first module was used in the soil mechanics courses at Rose-Hulman Institute of Technology and Saint Louis University to compare theoretical and observed behavior of shallow foundations. The second module was used in the foundation engineering course at Rose-Hulman Institute of Technology to illustrate the concepts of soil-structure interaction and the influence of near-surface soil on lateral behavior of deep foundations. 
    more » « less
  4. There is a critical need for more students with engineering and computer science majors to enter into, persist in, and graduate from four-year postsecondary institutions. Increasing the diversity of the workforce by inclusive practices in engineering and science is also a profound identified need. According to national statistics, the largest groups of underrepresented minority students in engineering and science attend U.S. public higher education institutions. Most often, a large proportion of these students come to colleges and universities with unique challenges and needs, and are more likely to be first in their family to attend college. In response to these needs, engineering education researchers and practitioners have developed, implemented and assessed interventions to provide support and help students succeed in college, particularly in their first year. These interventions typically target relatively small cohorts of students and can be managed by a small number of faculty and staff. In this paper, we report on “work in progress” research in a large-scale, first-year engineering and computer science intervention program at a public, comprehensive university using multivariate comparative statistical approaches. Large-scale intervention programs are especially relevant to minority serving institutions that prepare growing numbers of students who are first in their family to attend college and who are also under-resourced, financially. These students most often encounter academic difficulties and come to higher education with challenging experiences and backgrounds. Our studied first-year intervention program, first piloted in 2015, is now in its 5th year of implementation. Its intervention components include: (a) first-year block schedules, (b) project-based introductory engineering and computer science courses, (c) an introduction to mechanics course, which provides students with the foundation needed to succeed in a traditional physics sequence, and (d) peer-led supplemental instruction workshops for calculus, physics and chemistry courses. This intervention study responds to three research questions: (1) What role does the first-year intervention’s components play in students’ persistence in engineering and computer science majors across undergraduate program years? (2) What role do particular pedagogical and cocurricular support structures play in students’ successes? And (3) What role do various student socio-demographic and experiential factors play in the effectiveness of first-year interventions? To address these research questions and therefore determine the formative impact of the firstyear engineering and computer science program on which we are conducting research, we have collected diverse student data including grade point averages, concept inventory scores, and data from a multi-dimensional questionnaire that measures students’ use of support practices across their four to five years in their degree program, and diverse background information necessary to determine the impact of such factors on students’ persistence to degree. Background data includes students’ experiences prior to enrolling in college, their socio-demographic characteristics, and their college social capital throughout their higher education experience. For this research, we compared students who were enrolled in the first-year intervention program to those who were not enrolled in the first-year intervention. We have engaged in cross-sectional 2 data collection from students’ freshman through senior years and employed multivariate statistical analytical techniques on the collected student data. Results of these analyses were interesting and diverse. Generally, in terms of backgrounds, our research indicates that students’ parental education is positively related to their success in engineering and computer science across program years. Likewise, longitudinally (across program years), students’ college social capital predicted their academic success and persistence to degree. With regard to the study’s comparative research of the first-year intervention, our results indicate that students who were enrolled in the first-year intervention program as freshmen continued to use more support practices to assist them in academic success across their degree matriculation compared to students who were not in the first-year program. This suggests that the students continued to recognize the value of such supports as a consequence of having supports required as first-year students. In terms of students’ understanding of scientific or engineering-focused concepts, we found significant impact resulting from student support practices that were academically focused. We also found that enrolling in the first-year intervention was a significant predictor of the time that students spent preparing for classes and ultimately their grade point average, especially in STEM subjects across students’ years in college. In summary, we found that the studied first-year intervention program has longitudinal, positive impacts on students’ success as they navigate through their undergraduate experiences toward engineering and computer science degrees. 
    more » « less
  5. null (Ed.)
    Abstract The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or “distance” measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures. 
    more » « less