Performance-based inverse structural design of complex gradient triply periodic minimal surface structures based on a deep learning approach

Triply periodic minimal surface (TPMS) structures have excellent mechanical performance compared to other lattice structures, but the process of reversely designing complex TPMS structures according to desired requirements is difficult due to the multiple structural parameters. In this study, a new deep learning approach (Balance-CGAN), consisting of a forward property prediction network and an inverse structural design network, was proposed to reversely design the gradient energy absorbing TPMS structures. The forward fully connected neural network (FCNN) was employed as the mechanical model for predicting TPMS structural performance, while the conditional generative adversarial network (CGAN) was used for further inverse structural design, and both networks were integrated by the target loss function. The Balance-CGAN method was proved to be effective in designing TPMS structures that meet the target performance criteria, with the minimum design error for the specified target being 4.6 %. The forward prediction accuracy of FCNN directly impacted the inverse design accuracy of the Balance-CGAN, with the error between the actual and target performance of the structure rising from 4.6 % to 14.6 % as the forward prediction error increased from 3.8 % to 11.3 %. This work provides a reference for the design and additive manufacturing of new industrial energy absorbing TPMS structures with specific mechanical properties using machine learning techniques.