AI-tailored Wish Fulfillment

Whether it is a gasoline car or an electric vehicle, an SUV or a compact car, a sports car or a family vehicle – comfort is a crucial factor in the purchase of a car. The damping system, consisting of shock absorbers and additional springs, is essential for this. To ideally adapt the latter to the specific needs of certain cars and achieve the best possible comfort, iterative tuning processes and expert knowledge have always been a requirement. Commissioned by BASF Polyurethanes GmbH, a manufacturer of additional springs in the automotive industry, researchers at Fraunhofer IPK have now developed a process that aims to simplify the design of such additional springs using artificial intelligence. The goal is for AI to derive a suitable component directly from the customer’s requirements.

As a methodological approach to training an AI, the researchers used the CRISP-DM standard process. After an initial situational analysis, the available data is examined, and specific goals are derived. These serve as the basis for training and testing an AI model.

Additional springs are rotationally symmetrical and can therefore be represented in simplified models with few parameters. Compared to other training sets with more complex historical data, this approach significantly simplifies AI training.

Using the simple models, the researchers made systematic small changes. This allowed them to generate over 10,000 different designs from a single model. The team then simulated how these simplified and readjus­ted virtual springs behave under various loads. The data obtained from the simulations served as the training basis for a neural network.

In an initial random sample test, it was shown that the AI model is capable of generating components that come close to the specified requirements. In practice, this means that development teams, for example, could »feed« a new customer’s specifications into the AI and receive a functional initial design for an additional spring. They could then refine the design with detail adjustments.