Machine Learning-Based Topology Optimization in 3D Printing

The extremely lengthy computational times, coupled with the impracticality of the Finite Element Method (FEM)-based simulations, created a strong demand to accelerate the procedures of topology optimization involved in product development. This paper proposes a solution in the form of a deep Convolutional Neural Network (CNN) designed for 3D structural topology optimization, which predicts a structurein a relatively short amount of time without employing any iterative strategy. The methodology was created to empower users to incorporate a machine-learning approach to topology optimization in their designs while enabling the non-iterative strategy and increasing the efficiency of the process. The proposed approach is anticipated to significantly reduce computational time while improving the printed component’sstructural performance and design quality. This study offers a new perspective on topology optimization in 3D printing andhighlights the potential of machine learning in advancing theefficiency and precision of this process. The outcomes of this study illustrate that the proposed model possesses the capacity to conduct topology optimization of the cantilever beam structureat a significantly accelerated pace, as compared to specialized software showing a reduction in computation time, amounting to approximately 99%. These results hold promising implications for the time-critical engineering and optimization processes.