2023/06/13 by M.N. Baldwin, Baldwin, Martha, Nicholas A. Meisel +3
Engineering · #Additive Manufacturing Materials and Processes #Additive Manufacturing and 3D Printing Technologies #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #Machine Learning (cs.LG) #Manufacturing Process and Optimization #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2306.10055
openalex publication_date 2023/06/13 · openalex created_date 2023/06/22 · openalex updated_date 2026/07/28
Additive manufacturing is advantageous for producing lightweight components while addressing complex design requirements. This capability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be beneficial to use multiple, distinct lattice cell types, resulting in multi-lattice structures. In such structures, abrupt transitions between unit cell topologies may cause stress concentrations, making the boundary between unit cell types a primary failure point. Thus, these regions require careful design in order to ensure the overall functionality of the part. Although computational design approaches have been proposed, smooth transition regions are still difficult to achieve, especially between lattices of drastically different topologies. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells, examining the factors that contribute to smooth transitions. Through computational experimentation, it was found that the smoothness of transition regions was strongly predicted by how closely the endpoints were in the latent space, whereas the number of transition intervals was not a sole predictor.