Variational autoencoder-based near-net shape design optimization of panels reinforcing with the directed energy deposition additive manufacturing process

Purpose This study aims to propose a systematic reinforcement strategy in large, thin-walled panels with an image-based optimization framework that maximizes stiffness gains while minimizing added mass. By leveraging image-based inputs and generative modeling techniques, specifically variational autoencoders (VAEs), the approach enables efficient exploration and optimization of high-dimensional design spaces. The goal is to identify the optimal placement and configuration of local directed energy deposition (DED) reinforcements, fulfilling industrial demands for lightweight, yet rigid, structures through an automated design pipeline. Design/methodology/approach A path-optimization framework integrating a VAE with pretrained convolutional neural networks (CNNs) was developed to enhance thin-walled structure stiffness in large-sized parts. Latent variables of VAEs serve as continuous design parameters; the decoder generates candidate reinforcement-path images; and CNNs evaluate to compute stiffness and mass objectives. An optimization loop adjusts the latent codes to minimize mass under stiffness constraints, enabling nonparametric discovery of optimal local reinforcement geometries. Findings The VAE-based method achieved a 43.48% increase in panel stiffness with only a 2.81% increase in mass. Beyond straight-line reinforcements, the VAE generated curved paths, internal voids, and mid-panel start/end points, novel geometries not present in the training data, superior material utilization and structural performance. These results demonstrate that VAEs are highly effective at modeling complex design spaces and hold strong potential for generative design applications. Originality/value This work pioneers combining generative deep learning with an additive manufacturing process freedom to optimize structural reinforcements. Unlike traditional parametric or rule-based methods, the VAE uncovers creative reinforcement designs tailored to complex constraints, leveraging DED’s local deposition capabilities. Additionally, representing the design space through the continuous latent dimensions offered by VAEs enables the exploration of a significantly larger and more diverse design space compared to previous studies. The result is a flexible, efficient design tool for automotive, aerospace and defense applications seeking innovative, lightweight, high-performance, thin-walled components.