ABSTRACT
The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.
ABSTRACT
Research in undergraduate STEM education often requires the collection of student demographic data to assess outcomes related to diversity, equity, and inclusion. Unfortunately, this collection of demographic data continues to be constrained by socially constructed categories of race and ethnicity, leading to problematic panethnic groupings such as "Asian" and "Latinx." Furthermore, these all-encompassing categories of race and ethnicity exasperate the problematic "underrepresented minority" (URM) label when only specific races and ethnicities are categorized as URMs. We have long seen calls for improved outcomes related to URMs in undergraduate STEM education, but seldom have we seen our own understanding of what it means to be a URM go beyond socially constructed categories of race and ethnicity. If we aim to not only improve diversity outcomes but also make undergraduate STEM education more equitable and inclusive, we must reevaluate our use of the term "URM" and its implications for demographic data collection. The classifications of "underrepresented" and "minority" are more nuanced than simple racial categories. Though there has been development of alternative terms to URM, each with their own affordances, the main goal of this article is not to advocate for one term over another but rather to spark a much-needed dialogue on how we can "inclusify" our collection of racial and ethnic demographic data, particularly through data disaggregation and expanding our definition of what it means to be both "underrepresented" and a "minority" within STEM.
