Development of Material Extrusion Infill Optimization Approaches for Various-Resource Entities
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Additive manufacturing (AM) is an expanding research field, proportional to the increased adoption the industry is experiencing. With AM machines targeted towards industry level clients and hobbyists alike, the diversity in customers has become quite large. Given the benefit of AM being specialized at producing prototypes or parts in the mass customization categories, small scale producers can remain competitive with larger industries if they are able to compete on the design frontier. As the specialized software suites commonly used today in the new accelerated design cycle are quite expensive and require an advanced technical comprehension, beginner users are not able to benefit as greatly from them compared to the larger entities. To alleviate this disparity, this work targets the furtherment of optimization algorithms and methodologies applicable to all levels of an AM user’s ability. The introduction of an engineering-centric AM evaluation tree will allow non-technical managerial level users to evaluate the applicability of 3D printing for their applications. The design of a practical methodology that can be leveraged by any material extrusion (MEX) user will provide a foundation from which all parts with appreciable infill can be optimized. An expansion of this work will be subsequently provided that allows more technically advanced users to generate custom infill, improving on the structural performance available through commercial infill patterns. Lastly, the incorporation of manufacturing specific constraints into a topology optimization code will combine optimal topology results with practical manufacturing constraints for infill design, alleviating the need for substantial design reinterpretation. The culmination of the presented work provides an optimization framework for the hobbyist level users that have not historically seen many tools available to them, while simultaneously furthering the realism of constraints found in optimization algorithms, producing more manufacturable final results. Overall, advances in the research are presented at all levels of AM ability which stand to further the industry as a whole.
URI for this recordhttp://hdl.handle.net/1974/28756
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