A set of monotonic tensile tests was performed on 3-D printed plastics following ASTM standards. The experiment tested a total of 13 “dog bone” test specimens where the material, infill percentage, infill geometry, load orientation, and strain rate were varied. Strength-to-weight ratios of the various infill geometries were compared. It was found through tensile testing that the specific ultimate tensile strength (MPa/g) decreases as the infill percentage decreases and that hexagonal pattern infill geometry was stronger and stiffer than rectilinear infill. However, in finite element analysis, rectilinear infill showed less deformation than hexagonal infill when the same load was applied. Some design guidelines and future work are presented.
Recent interest in improving pedagogical approaches in science, technology, engineering, and mathematics (STEM) fields has stimulated research at many universities. Several educational methodologies are reviewed in the context of manufacturing and through the lens of sustainability. It is found that there is a need to identify and understand the STEM educational challenges, and to assess the usefulness of existing methodologies using case-based analyses. In particular, this research aims to support student learning in manufacturing engineering through real-time process evaluations. A pedagogical framework is presented that can assist engineering educators in developing learning modules in support of this goal. The framework encompasses four steps: define the learning outcomes, create instructional resources, create active learning resources, and create a summative assessment mechanism. The framework emphasizes engagement of manufacturing engineering students in psychomotor learning, which remains a challenge due to the high cost of instructional laboratories. The framework is applied to develop a participatory pedagogy for manufacturing courses through the use of computer numerical control of manufacturing operations, and real-time monitoring, visualization, and data analysis of machine energy use. The framework is demonstrated for upper-level undergraduate and graduate manufacturing engineering courses at two universities (i.e., Computer-Aided Design and Manufacturing at Oregon State University and Precision Manufacturing at University of California, Berkeley). It is found that the framework can effectively support learning module development in manufacturing engineering education.
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