| Kurzfassung
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Manufacturing optimization is crucial for organizations to remain competitive in the market. However, optimizing complex processes, such as textile forming, can be challenging and resource-intensive. Surrogate-based optimization can efficiently optimize manufacturing process parameters by using simplified models to guide the search for optimal parameter combinations. Moreover, using a Bayesian deep neural network allows for incorporation of uncertainty estimates into the model, which can speed up the optimization process. In this work, the surrogate model, a Bayesian deep convolutional neural network, is proposed to train with all available process parameters to predict the shear angle of a textile element. It is believed that predicting detailed process results incorporating background information into the surrogate model can lead to greater efficiency and increased product quality.
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