Surrogate Model Based Process Parameters Optimization of Textile Forming: Unterschied zwischen den Versionen

Aktuelle Version vom 19. März 2023, 20:49 Uhr

Vortragende(r)	Aleksandr Eismont
Vortragstyp	Proposal
Betreuer(in)	Bela Böhnke
Termin	Fr 31. März 2023
Vortragsmodus	in Präsenz
Kurzfassung	Manufacturing optimization is crucial for organizations to remain competitive in the market. However, complex processes, such as textile forming, can be challenging to optimize, requiring significant resources. Surrogate-based optimization is an efficient method that uses simplified models to guide the search for optimal parameter combinations of manufacturing processes. Moreover, incorporating uncertainty estimates into the model can further speed up the optimization process, which can be achieved by using Bayesian deep neural networks. Additionally, convolutional neural networks can take advantage of spatial information in the images that are part of the textile forming parameters. In this work, a Bayesian deep convolutional surrogate model is proposed that uses all available process parameters to predict the shear angle of a textile element. By incorporating background information into the surrogate model, it is expected to predict detailed process results, leading to greater efficiency and increased product quality.

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-|kurzfassung=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.
+|kurzfassung=Manufacturing optimization is crucial for organizations to remain competitive in the market. However, complex processes, such as textile forming, can be challenging to optimize, requiring significant resources.  Surrogate-based optimization is an efficient method that uses simplified models to guide the search for optimal parameter combinations of manufacturing processes. Moreover, incorporating uncertainty estimates into the model can further speed up the optimization process, which can be achieved by using Bayesian deep neural networks. Additionally, convolutional neural networks can take advantage of spatial information in the images that are part of the textile forming parameters. In this work, a Bayesian deep convolutional surrogate model is proposed that uses all available process parameters to predict the shear angle of a textile element. By incorporating background information into the surrogate model, it is expected to predict detailed process results, leading to greater efficiency and increased product quality.
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