Self-optimizing Production Technologies

Klocke, Fritz; Abel, Dirk; Gries, Thomas; Hopmann, Christian; Loosen, Peter; Poprawe, R.; Reisgen, Uwe; Schmitt, Robert; Schulz, Wolfgang; Abels, Peter; Adams, O.; Auerbach, Thomas; Bobek, Thomas; Buchholz, Guido; Döbbeler, Benjamin; Frank, Daniel; Heinisch, Julian; Hermanns, Torsten; Gloy, Yves-Simon; Keitzel, Gunnar; Kemper, Maximilian; Martel, Diana Suarez; Reimer, Viktor; Reiter, Matthias; Saggiomo, Marco; Schwenzer, Max; Stemmler, Sebastian; Stoyanov, Stoyan; Thombansen, Ulrich; Veselovac, D.; Willms, Konrad

doi:10.1007/978-3-319-47452-6_9

Cited by 3 publications

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to all manufacturing companies, the industry is exposed to new challenges due to global change. For example, batch sizes are becoming increasingly smaller due to individual customer requirements [2,3], experts are retiring [4] and qualified employees are becoming increasingly difficult to find [5]. As the textile industry is characterized by manual labor and expert knowledge dependency, these challenges pose a threat to a large number of companies.…”

Section: Introductionmentioning

confidence: 99%

Objectifying Machine Setup and Parameter Selection in Expert Knowledge Dependent Industries Using Invertible Neural Networks

Müller

Posada-Moreno²,

Pelzer

et al. 2022

Lecture Notes in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

The textile industry is one of the oldest and largest industries in the world. The fields of application for textile products are diverse. Although the technologies for manufacturing textiles are extensively researched, the industry is still highly dependent on expert knowledge. To date, manual process- and machine adjustments and quality control are the norms rather than the exception. Heat setting is used in the process chain to dissolve or selectively introduce tensions from the weaving or knitting process and to prepare the products for digital printing. The correct setting of the machine depends on a large number of different materials-, processes- & environmental parameters. For each product, the machine has to be set up again by an experienced textile engineer. To ease the training for new workers and shorten the machine setting process, this study aims to use machine learning to facilitate and objectify the setting of the heat-setting process. Machine parameters are generated using an invertible neural network (INN) based on pre-defined target parameters. The results can be used to identify trends in machine settings and respond accordingly. Thus, a reduction of machine setting time could be realized.

show abstract

Section: Introductionmentioning

confidence: 99%