An automatic optimization method for minimizing supporting structures in additive manufacturing
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The amount of supporting structure usage has been a major research topic in layer-based additive manufacturing (AM) over the past years as it leads to increased fabrication time and decreased surface quality. Previous studies focused on deformation and topology optimization to eliminate the number of support structures. However, during the actual fabrication process, the properties of shape and topology are essential. Therefore, they should not be modified casually. In this study, we present an optimizer that reduces the number of supporting structures by identifying the prime printing direction. Without rotation, models are projected in each direction in space, and the basis units involved in the generation of support structures are separated. Furthermore, the area of the supporting structures is calculated. Eventually, the prime printing direction with minimal supporting area is obtained through pattern-searching method. The results of the experiment demonstrated that the printing area was reduced by up to 60% for some cases, and the surface quality was also improved correspondingly. Furthermore, both the material consumption and fabrication time were decreased in most cases. In future work, additional factors will be considered, such as the height of the supporting structures and the adhesion locations to improve the efficiency of this optimizer.
KeywordsPrinting direction Optimization Supporting structures
This work was supported from the National Key R&D Program of China (Grant No. 2017YFB1104100), the National Natural Science Foundation of China (Grant Nos. 81971709, 81828003), the Foundation of Ministry of Education of China Science and Technology Development Center (Grant No. 2018C01038), the Foundation of Science and Technology Commission of Shanghai Municipality (Grant Nos. 19510712200, 16441908400), and Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research (Grant Nos. YG2016ZD01, ZH2018ZDA15).
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