Contextualization of design tasks for automation of the embodiment design and dimensioning phase with Artificial Intelligence

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Due to the high competitive pressure in high-wage countries, there is an increasing need to automate routine activities from the product development process. At the same time, rapid technological advancements are enabling the increasing industrial use of state-of-the-art tools from the research field of artificial intelligence (AI). Often, these are integrated into the product development process as black boxes without the product developers having a detailed understanding of the algorithms. Consequently, the behavior and results of the AI solution can only be poorly interpreted, resulting in erroneous predictions. Therefore, the AI methods in product development should be more closely aligned with the mindset of the product developers, so that the generated results and the journey to them can be directly tested. In preliminary work of the applicant it could be shown that the use and benefit of the AI method Reinforcement Learning in product development offers great potential for the given task. In Reinforcement Learning, an agent performs part of the design tasks of product developers. Therefore, the goal of the project is to increase the understandability of automatically improved products by means of reinforcing learning. Special focus will be put on the virtual geometry generation in product development without disadvantages for the component design. Three research questions will be answered, which cover different aspects. First, the focus is on the extension of the state description, which should be brought closer to the needs of the product development. Afterwards, a new exploration strategy should be developed, which is oriented towards the exploration of experienced product developers. With the help of the last question the extent to which it is possible to map implicit relationships in the development process via intrinsic rewards is analyzed. Fiber-reinforced plastic (FRP) components serve as demonstrator components because their design is based on complex interrelationships. However, in the design of FRP components, extensive expertise can be drawn on at the applicant, which forms the basis for the research project. With an automation tool for improved automated geometry generation and increased comprehensibility of the performed actions, there is enormous potential to improve the product development process. On the one hand, product developers with less experience in using AI methods can use the researched tool and better understand the changes in the product shape. On the other hand, standard or routine activities can be automated and thus lead to time and cost savings in the design of products.