ITWM: Machine Learning in the textile industr...
ITWM

Machine Learning in the textile industry

ITWM
CFD simulation of a virtual bobbin in a dye bath
CFD simulation of a virtual bobbin in a dye bath

The demands placed on the textile industry are changing dramatically. The trend is towards individualization in many areas, similar to car purchasing, for example. Consumers are increasingly demanding tailor-made products.

This change in consumer behavior is lucrative for European textile companies, since the customer-specific manufacture of products with small batch sizes leads to a relocation of production back to Europe. However, this requires the digitalization of production, which the Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern/Germany, supports with hybrid simulation-based Machine Learning (ML) methods.

Not just databased machine learning


In databased machine learning, statistical learning algorithms were developed that recognize patterns and structures in given data. The quality of the ML algorithms depends decisively on the quality and quantity of the available data. In the textile industry, enough measurements are usually collected for quality control. However, in the rarest of cases there is sufficient usable data available which links the process parameters with the product quality. This means that we cannot use purely data-driven machine learning - especially in plant and process optimization for customer-specific production processes.

Hybrid simulation-based machine learning

In order to design and optimize production processes in the textile industry using ML, a hybrid approach were developed and used. For the design of processes and products, the textile industry has extensive experience. This expertise was formalized by describing the processes using physical models and then implementing them numerically. Simulations then provide the missing data to develop suitable ML algorithms and to interlock them with existing measurements. In this concept ML closes the gap between physically based simulation of production processes and the - in many cases not accessible to a physical model - quality measure of the end products.
The new hybrid ML process will be demonstrated at the Techtextil using the optimization of cross-winding machines as an example for a better dyeing of the wound yarn bobbins as part of the AiF DensiSpul project.


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