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  1. Abstract

    The support structures required in many forms of additive manufacturing are often seen as waste that is tolerated as necessary. In metal additive processes, cost is frequently reduced by minimizing the amount of support structures needed to produce a part so that in turn, material use is decreased. However, there still exists the challenge of generating parts that are not deformed by the stresses created in the process. In this case study, support structures were leveraged to address deformation. A part was printed via direct metal laser melting with supports with a high grouping density in areas of high anticipated deformation in order to stiffen the part to prevent deformation. Then, they were printed again with a low grouping density to allow the part to relax and reduce stress. Combinations of support strategy and leaving supports on during post processing were used to investigate the effects of keeping or removing the supports in post-print operations such as surface treatment. The two optimized support strategies saw a lower deformation than the baseline approach to supports, and the releasing strategy was closest to the reference solid model with a 26% reduction in average deformation. The results suggest that the support structures in additively manufactured parts have a different impact on the part than the original intent of the supports to simply alleviate a process requirement. The support structures should be used to impact the final part geometry.

     
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  2. K. Ellis, W. Ferrell (Ed.)
    Work-related musculoskeletal disorders contribute to significant loss in productivity and higher costs for employers. This research utilizes body-worn motion and hand-worn force sensors to provide non-intrusive and continuous recognition of tasks, estimate force exertion, and evaluate if operators are working in safe ergonomic ranges. Work-related motions such as lifting, carrying, pulling, and pushing are measured with varied loads up to 10 kg, and then recognized performed using the IBM Watson cloud service platform. The experiments use sequential and quasi-static postures and mimic those commonly found in an automotive assembly environment. Classification performance included generating 70 input features based on 6 motion and 4 force inputs and three of the resulting classifier had a greater than 90% accuracy in simultaneously classifying both the weight being carried and the task being completed. Future work includes measuring non-quasi-static motions and integrating additional sensors, such as those from smart tooling, which tracks tool position and orientation, to provide a continuous and unobtrusive evaluation of worker exertion and risk of musculoskeletal disorder. 
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  3. Keywords : DMLM, Additive Manufacturing, Support Structures, Gas Turbines 
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    This study explored seven engineering graduate students’ collaborative problem-solving (CPS) skills while working in interdisciplinary teams. Students worked in two different teams, in face-to-face and online environments, to solve complex manufacturing design challenges posed by their instructor. The students were assessed using an observational rubric with four dimensions: peer interactions, positive communication, tools and methods and iteration and adaption, and scored via each dimension’s associated attributes, and subsequently interviewed. Six students scored emergent or proficient in CPS and had slightly higher CPS scores during the second observation. One student demonstrated a limited ability for CPS and the observable CPS skills decreased during the project. Interviews revealed the importance of (1) relying on instructor and student chosen technologies for collaborative tasks, (2) recognising and drawing on peer expertise early in the project, (3) building trust during and outside of team meetings and (4) valuing off-site and online collaborative work. Findings advance the understanding of how graduate students working in interdisciplinary teams rely on particular features of collaboration to solve engineering design challenges, which may assist in developing future skills and fostering productive teamwork. 
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