## 1 Introduction  
Machine learning models have recently been used in product and engineering design in almost every field [1; 2; 3]. In particular, developing and using deep-learning-based generative design methods has accelerated design processes such as inspiration, idea generation, concept generation, evaluation, and optimization [4; 5]. By using these methods, better and comprehensive designs can be created in a very short time and with minimal designer intervention. Generative deep learning models can include all design properties within a feature space, a simple representation of the design space. The feature space makes it possible to generate new concepts by capturing the critical points of the design [6].  
A generative adversarial network (GAN) is one of the current data-driven generative design systems. The ability of the GAN to generate new data (text, images, 3D models, etc.) is utilized in design studies. One of the aims of this study was 3D concept generation. Since 3D concepts contain more visual and physical features than 2D concepts, the level of detail is higher. This makes data-driven generative design research that generates 3D models critical [7]. Classical GAN models can discover form features depending on the design visualization. However, a good design representation should consider form and function features. The form includes the geometric and topological properties of the product. Function refers to the design purpose of the product. Designers can understand the relationship between form and function. However, it is difficult for artificial intelligence to grasp such complex relationships. For this reason, generative design studies generally aim to produce new forms. The concepts created here are far from being functional/structural workpieces and can only be used with additional modifications and improvements. These extensive challenges constitute a new and broad area of research.  
In this study, a generative deep learning model and finite element analysis are combined to evaluate the form and function properties of the generated designs. While 3 DGAN generates new data, finite element analysis (FEA) is used to evaluate the mechanical properties of the generated data. At the end of the evaluation, design options that are good in terms of form, weight, and strength are added back to the GAN training set. Thus, better designs improve the quality of the training set, and the new 3D models produced have enhanced functional properties. The main objective of this study was to develop an autonomous approach to improving the functional properties of 3D models generated by deep-learning-supported generative design method. In that context, all design components that transfer load, force, or power were evaluated by FEA and redesigned if necessary. Unlike other studies, this study combined data-driven generative design and FEA methods for the first time. Thus, the mechanical properties of structural components generated by 3 DGAN were analyzed.  
Another innovative aspect of this study is that it provides a new option for the conditioning of data-dependent generative design models. These models are known to generate data that reflect the distribution of the training dataset. With repeated design and evaluation steps, the training dataset's general properties are improved, and high mechanical properties are achieved for the generated data. By conditioning the mechanical properties of generative models, this can create a potential area for deep learning studies in engineering design.