Predicting cycle times in textile manufacturing using artificial neural network

Erdem Onaran*, Seda Yanık

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


In textile manufacturing systems, manual labor is considered as necessity due to difficulty of working with a non-rigid material and constantly changing product types. Using robots which have the ability to work with such materials are still quite expensive compared to manual-labor. Since textile processes depend on human capabilities, it is hard to predict processing times, which is essential for production planning. Many textile manufacturers use time study methods for planning, however it only considers the motion related with the sewing process, causing decreased accuracy for predicted cycle times. Yet, in reality, there are many factors affecting the cycle times, such as type of sewing machine, abilities of workers, material (e.g. fabric) type and product design. Including all these factors increase the complexity of the time model, but they can be necessary to increase prediction accuracy. In this study, multilayer perceptron, which is one of the most widely used approaches in machine learning, is used to predict cycle times of a common operation in textile manufacturing, as many studies have shown that machine learning methods are more effective while dealing with many variables and complex relationships compared to statistical methods.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783030237554
Publication statusPublished - 2020
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey
Duration: 23 Jul 201925 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2019

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • Cycle time prediction
  • Neural networks
  • Textile manufacturing


Dive into the research topics of 'Predicting cycle times in textile manufacturing using artificial neural network'. Together they form a unique fingerprint.

Cite this