Horseshoe Lattice Property-Structure Inverse Design Based on Deep Learning

抄録

Lattice structures, characterized by their exceptional strength-to-weight ratios and energy absorption capabilities, have paved the way for pioneering designs in additive manufacturing (AM). To fully harness the potential of AM, robust inverse design methodologies are essential. In this study, a novel FEM-LSTM based lattice structure inverse design framework was proposed for horseshoe lattice structures characterized by Length (L), Radius (R), and Angle (A) to establish the structure-performance response. Using finite element analysis, a substantial dataset with distinct geometries and mechanical responses was meticulously furnished for training. Delving deeper into modeling, we developed an autoencoder framework anchored in long short-term memory (LSTM) networks, designed to adeptly decode the temporal intricacies of stress-strain attributes and seamlessly encode sequence characteristics. Compared to traditional GPR models and DNN models, the proposed model’s predictability increased by 9% and 7%, respectively, which is attributable to the exceptional capability of LSTM structure in handling time-series data. Our model, being versatile, can seamlessly integrate multiple stress-strain inputs, rendering precise geometric parameters that resonate with tailored design specifications. Such a streamlined approach effectively supplants the conventionally tedious iterative forward design and exhaustive simulation phases. In summation, the model emerges as a swift conduit for bespoke inverse design pertaining to lattice structures. And the paradigm of discerning time-series correlations through LSTM autoencoders holds vast potential across diverse time-dependent properties inherent to materials science.