Search results

The purpose of the paper is to propose a homogenization-based topology optimization method to optimize the design of variable-density cellular structure, in order to achieve lightweight design and overcome some of the manufacturability issues in additive manufacturing.

First, homogenization is performed to capture the effective mechanical properties of cellular structures through the scaling law as a function their relative density. Second, the scaling law is used directly in the topology optimization algorithm to compute the optimal density distribution for the part being optimized. Third, a new technique is presented to reconstruct the computer-aided design (CAD) model of the optimal variable-density cellular structure. The proposed method is validated by comparing the results obtained through homogenized model, full-scale simulation and experimentally testing the optimized parts after being additive manufactured.

The test examples demonstrate that the homogenization-based method is efficient, accurate and is able to produce manufacturable designs.

The optimized designs in our examples also show significant increase in stiffness and strength when compared to the original designs with identical overall weight.

Given the disruption of the COIVD-19 pandemic in higher education, this study seeks to understand possible changes in students’ ratings and textual reviews of higher education institutions posted on Niche College Rankings (niche.com) prior to and after the COVID-19 pandemic.

This study utilized a text analytics technique to identify the positive and negative keywords of students’ sentiments expressed in their textual reviews provided on niche.com. After identifying the positive and negative sentimental keywords, this study performed ordinal logistic regressions and analyzed the statistical effects of these positive and negative sentimental keywords on the types of student ratings of a higher education institution.

Results from 15,666 online reviews provided by students on niche.com indicate the following. First, eight positive sentimental keywords such as “outstanding” and “love” have a significant impact on students’ positive ratings of a higher education institution prior to COVID-19, whereas eight positive sentimental keywords such as “amazing” and helpful” have a significant impact on students’ positive ratings of a higher education institution after COVID-19. Second, twenty-eight negative sentimental keywords such as “difficult” and “frustrating” have a significant impact on students’ negative ratings of a higher education institution prior to COVID-19, whereas thirty negative sentimental keywords such as “complex” and “hate” have a significant impact on student negative ratings of a higher education institution after COVID-19.

This study is one of the first few studies investigating higher education institution ratings and reviews provided by students. Additionally, this study provides an understanding of student positive and negative sentiments expressed in textual reviews posted prior to and after the COVID-19 pandemic. By doing so, this study provides a basis for future research seeking to understand student textual reviews of higher education institutions. Additionally, this study offers higher education administrators some recommendations that may foster student positive campus experience while minimizing negative sentiments.