The Effect of a Topology Optimization based Generative Design tool on the Engineering Design Process

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In the engineering industry, all structural parts have to be designed as efficient and lightweight as possible. Traditionally, the design process has been carried out through manual design iterations, which can be time-consuming and require significant engineering expertise. Over the last decades however, several computational design techniques like Topology Optimization and Generative Design have been developed to support engineers in the structural part design process. Even though these techniques can have a positive influence on the design process, they both also have their downsides. Topology Optimization only gives a single result that is often a local optimum, influenced by boundary conditions and numerical settings. Commercial Generative Design tools explore multiple design options in a single run using AI algorithms, but need cloud-based systems to carry out their demanding simulations which still take several hours per run. It is however expected that a combination of the two, a Topology Optimization based Generative Design approach in the form of an auxiliary tool, has potential to improve the early stages of the design process even more. With such a design approach, multiple design solutions are explored quickly to study the effect of boundary conditions or numerical settings. This can help designers by giving direction and insight in trade-offs between multiple objectives, early on in the design process when design decisions still have the highest impact.

The goal for this research was therefore to research the effect of such a Topology Optimization based Generative Design approach on the design performance and experience. In order to do so, a robust and user-friendly TOP-GD tool was created. In this tool, multiple design solutions are explored quickly by implementing a batch-run setup that varies several chosen parameters, without needing to manually run several optimizations consecutively. Calculations are done with a simple TO script using coarse geometries, and without taking into account manufacturing methods yet. This asks for less demanding, detailed and complicated calculations than AI-based Generative Design tools currently offer, while at the same time moving from a single TO result to generating a range of candidate solutions. A lot of effort was put in the user-friendliness of the TOP-GD tool, enabling an easy workflow for the setup of design problems and a clear presentation of the results by means of a simple GUI.

The use of the TOP-GD tool in the design process was evaluated in an experiment, where it was compared with a more simple TO tool and a basic manual design approach using just pen and paper. This was done by giving the participants of the experiments three simple design assignments, that they had to carry out using each of the design approaches one by one. Evaluation of the approaches was done by comparing the design performance, and assessing the design experience with a survey and using Eye-tracking techniques.

The results of this experiment did not show enough evidence to conclude that the different design approaches had an effect on the design performance for the simple assignments executed during the experiment. However, the results of the survey show a clear positive impact of both the TO tools on the design experience, compared to manually designing. Furthermore, the TOP-GD tool has the largest positive impact on the design experience and its use in the design process is considered a big improvement, especially in quickly exploring new design directions and creating overview. This confirms the expectation that a Topology Optimization based Generative Design approach has a positive effect on the early stages of the design process. The differences found with Eye-tracking between the TO tools support this, although a more extensive experiment should be done to convincingly confirm this conclusion.