## 2 Literature Review
(2020) proposed the Continuous Conditional Diverse Generative Adversarial Network (PcDGAN) model to generate new designs that meet the targeted performance requirements. The authors stated that the design space coverage capability of PcDGAN is higher than that of standard GAN models. In another study, [PERSON] et al. (2020) proposed a Range-Constrained Generative Adversarial Network model to meet engineering design constraints. This proposed model aims to generate data that satisfy the targeted design constraints with a self-expansion approach and a new loss function. In another training success improvement study, [PERSON] et al. tried to find a solution to the problem of generative models generating faulty geometries. The proposed new training method aims to outperform the generative model by utilizing erroneous data points ([PERSON] et al., 2020). Another field where generative design methods have been applied is that of design options' evaluation and optimization process ([PERSON] et al., 2020; [PERSON] and [PERSON], 2020). In one of these studies, [PERSON] and [PERSON] (2020) proposed a convolutional neural network (CNN)-based transfer learning model to overcome the computational costs of topology optimization. The authors aimed to process high-resolution 3D design space in real-time with fewer samples than traditional deep learning methods. In another study, [PERSON] and [PERSON] (2020), addressing high computational costs, created a model that can generate 3D geometries with an approach based on Wasserstein generative adversarial networks (WGANs). This model demonstrates the potential of deep learning to significantly reduce iterative optimization processes in design. [PERSON] et al. (2020) developed a functional generative design model using GANs and computational fluid dynamics (CFD) methods. The authors introduce an iterative process of CFD evaluation of 3D aircraft models generated by GANs. This results in good aircraft designs with a low drag coefficient. Although the presented approach obtained coarser results than topology optimization methods, it converged faster. In another design evaluation study, [PERSON] et al. (2020) proposed a CNN model that predicts the success of a product from product sheets generated for design competitions. In another study, [PERSON] et al. (2020) aimed to produce aesthetic wheel designs with high engineering performance by integrating topology optimization and boundary equilibrium GANs (BeGANs). In this study, the data obtained from topology optimization were incorporated into the training data to produce high-performance models. The authors claim this approach provides lower-cost optimization with minimal design and engineering domain knowledge. There have been many studies in the literature on incorporating generative deep learning methods into the engineering design process. These studies offer various applications, from idea and concept generation to design optimization. The potential of GANs in the generative design process is remarkably emphasized in these studies. However, these studies focus predominantly on the aesthetic aspects of design, often overlooking the integration of functional or mechanical properties. This creates a gap in the application of these systems to fields where functionality is as crucial as design, such as in engineering and industrial design. Additionally, while these papers demonstrate the capabilities of generative models like GANs, they often do not fully leverage the potential of these models for improving the mechanical performance of the generated designs. Therefore, more studies are needed that aim to improve the structural strength performance of work pieces produced with GANs. Accordingly, the design framework proposed in this study verifies and enhances the structural strength of innovative and creative design options created with GANs using FEA. An autonomous approach has been developed to add functional features to deep-learning-supported generative design methods.