Remodeling of the Drone Chassis Designed for Additive Manufacturing Method According to Topology Optimization
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DOI:
https://doi.org/10.59287/icaens.1085Keywords:
Topology Optimization, Layered Manufacturing, Drone Design, Advanced Manufacturing, Static AnalysisAbstract
In the last few years, additive manufacturing methods have made remarkable advancements, and with the emergence of next-generation additive manufacturing techniques, the variety of raw materials has increased, and part design criteria have been improved. Particularly, the development of metal additive methods has made revolutionary contributions to the manufacturing field and offered a new and unique perspective on design. While traditional manufacturing methods require adherence to specific and standardized design criteria, there are no rules or standards that must be followed in additive manufacturing, except for a few criteria. This is one of the most significant features that distinguishes additive manufacturing methods from traditional methods. The freedom of design provided by additive manufacturing enables much more successful mass reduction in the parts produced using these methods. Mass reduction is expected to be minimal in aerospace, automotive, medical, and dental applications. In this study, first, a drone chassis was designed using NX software, and static analysis was performed in Ansys program by defining specific boundary conditions for the designed drone chassis. Then, based on the conducted static analysis, topology optimization was carried out in Ansys program to achieve mass reduction. As a result of the topology optimization performed, it was observed that the obtained geometry decreased from 5.8394 kg to 1.288 kg. After the topology optimization, the obtained geometry was redesigned in the NX environment, and static analysis was applied to the redesigned geometry. Considering the results of the applied static analysis, it was observed that the part can operate safely under working conditions. It was determined that the mass was reduced by approximately 78% after topology optimization, indicating that topology optimization was highly successful in the mass reduction process.