Search for: All records

The Louisiana State University College of the Coast & Environment (LSU CC&E) developed a multi-faceted strategic initiative to increase its diversity, equity, and inclusion. Elements of this initiative began one-by-one over the last decade and were aggregated into a comprehensive implementation plan—the 2020 CC&E Diversity, Equity, and Inclusion Action Plan. Based on best practices in the “physical sciences” (i.e., geosciences, including oceanography), the plan aims to augment recruitment and retention using limited resources and personnel. The long-range goal is to increase the number of PhD graduates from underrepresented groups by employing strategies that start at the 6–12th grade level. The plan involves a change in culture, clear communications about needs and goals, and active participation and engagement of CC&E faculty, students, staff, supporters, and partners. This plan is increasing the diversity of enrollment in our academic programs and will ultimately lead to more students with both undergraduate and graduate degrees. 

Abstract Machining-induced residual stresses (MIRS) are a main driver for distortion of thin-walled monolithic aluminum workpieces. Before one can develop compensation techniques to minimize distortion, the effect of machining on the MIRS has to be fully understood. This means that not only an investigation of the effect of different process parameters on the MIRS is important. In addition, the repeatability of the MIRS resulting from the same machining condition has to be considered. In past research, statistical confidence of MIRS of machined samples was not focused on. In this paper, the repeatability of the MIRS for different machining modes, consisting of a variation in feed per tooth and cutting speed, is investigated. Multiple hole-drilling measurements within one sample and on different samples, machined with the same parameter set, were part of the investigations. Besides, the effect of two different clamping strategies on the MIRS was investigated. The results show that an overall repeatability for MIRS is given for stable machining (between 16 and 34% repeatability standard deviation of maximum normal MIRS), whereas instable machining, detected by vibrations in the force signal, has worse repeatability (54%) independent of the used clamping strategy. Further experiments, where a 1-mm-thick wafer was removed at the milled surface, show the connection between MIRS and their distortion. A numerical stress analysis reveals that the measured stress data is consistent with machining-induced distortion across and within different machining modes. It was found that more and/or deeper MIRS cause more distortion. 

Near surface residual stress (NSRS) induced by machining (e.g., milling) is known to drive distortion in machined aluminum, particularly in thin complex geometries with tight tolerance requirements where large distortion is undesirable. The understanding and characterization of NSRS in milled aluminum parts is important and should be included in the design and manufacturing process. There exist a variety of experimental tests characterizing these stresses. The objective of this paper is to assess the quality of three experimental methods for evaluating NSRS in prismatic aluminum parts subject to various milling parameters. The three methods are: hole-drilling, slotting, and x-ray diffraction, all of which include incremental material removal. The aluminum parts are cut from stress-relieved plate, AA7050-T7451. A combination of milling table and tool speeds are used to machine a flat surface in the parts. Measurements are made at specified locations and depths on each part. NSRS data from the hole drilling and slotting measurements were comparable; NSRS data from x-ray diffraction differed and was less repeatable. NSRS data for different milling parameters shows that the depth of NSRS increases with feed per tooth but is unaffected by different cutting speeds. 

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.