Accelerating the design of the effective surface of pressing tools with probabilistic inverse modeling approaches

Abstract

In the design and production of press part components, the tool development process is an essential step to fulfil the required quality criteria. Conventionally, this is achieved based on expert knowledge to design the effective surface with respect to the multitude of physical, procedural, and human influences. Thus, several iterations in the tool development process are usually required, which are costly and can lead to bottlenecks within product design cycles. To accelerate this tool design, we propose a diffusion model architecture to inversely design the necessary effective tool surface given a desired geometry of the press part. This diffusion model is able to reduce the generalization issues of classical machine learning approaches by leveraging the attention mechanism both in the spatial and temporal dimension of the underlying forming process. The applicability of a similar diffusion model has already been shown in previous applications for the inverse‐design of metamaterials and this work further demonstrates diffusion models as a suitable model candidate for the inverse‐design of 3D‐geometries. For model training, finite element simulations containing the time series of deformation states during the forming process were used. Furthermore, different geometry variations of part and tool as well as relevant press process parameters were used in the training. With the procedure demonstrated in this study, a future‐oriented support for the tool development process has been shown, enabling further developments towards a time‐ and cost‐efficient production of press tools.