Industrie 4.0-Technologieansätze zur Effizienzsteigerung im Schmelzspinnprozess

  • Industry 4.0 technology approaches for efficiency increase of the melt spinning process

Debicki, Lukasz Mikolaj; Gries, Thomas (Thesis advisor); Schmitt, Robert H. (Thesis advisor)

Aachen : Shaker (2020)
Book, Dissertation / PhD Thesis

In: Textiltechnik/ Textile Technology
Page(s)/Article-Nr.: IV, 243 Seiten : Illustrationen, Diagramme

Dissertation, RWTH Aachen University, 2020


Polyester filament yarns account for the largest share of manmade fibre production and are used in clothing, technical applications (e.g. safety belts) and carpets. The most important and economical process for the production of filament yarn is the melt spinning process. Economic success in the 21st century is characterised by the use of industry 4.0. Industry 4.0 means the mass combination of information and communication technologies with industrial production. Industry 4.0 leads to improved efficiency in development, production, service and marketing. The linking of industry 4.0 with filament yarn production in the melt spinning process is of particular interest for increasing productivity and has hardly been researched to date. The associated potential is therefore not fully exploited. The aim of this work is to contribute to the technological development of industry 4.0 applications in melt spinning process. The newly developed technology shortens the implementation time of first industry 4.0 solutions to reduce production costs in manufacture of filament yarn. In order to achieve this goal, the first step is to conduct a study of existing industry 4.0 solutions. The approaches found are then transferred into concrete concepts for the use in melt spinning process. In a second step, a process data based soft sensor is developed, based on the insight that process data monitoring is the key component for an industry 4.0 implementation in melt spinning process. The developed sensor enables data-driven quality monitoring of the process by error detection in spin-finish application. As a third step, two further industry 4.0 approaches are developed specifically for the melt spinning process. Both approaches are also based on data. An approach for predictive maintenance using the example of monitoring the spin-filter service life and an approach for error tracing in the textile process chain are investigated.