Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard

authored by: Nils Doede, Paulina Merkel, Mareile Kriwall, Malte Stonis, Bernd Arno Behrens
Abstract: Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.
Organisation(s): Institute of Metal Forming and Metal Forming Machines
External Organisation(s): Institut für integrierte Produktion Hannover (IPH)
Type: Article
Journal: Production Engineering
No. of pages: 12
ISSN: 0944-6524
Publication date: 03.07.2024
Publication status: E-pub ahead of print
Peer reviewed: Yes
ASJC Scopus subject areas: Mechanical Engineering, Industrial and Manufacturing Engineering
Electronic version(s): https://doi.org/10.1007/s11740-024-01298-8 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{841a2ffc7e2d4b17b75d5249570d9746,
title = "Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard",
abstract = "Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.",
keywords = "Hot forming, Predictive maintenance, Process monitoring, Quality management, Wear",
author = "Nils Doede and Paulina Merkel and Mareile Kriwall and Malte Stonis and Behrens, {Bernd Arno}",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",
year = "2024",
month = jul,
day = "3",
doi = "10.1007/s11740-024-01298-8",
language = "English",
journal = "Production Engineering",
issn = "0944-6524",
publisher = "Springer Verlag",
}