Relating wear stages in sheet metal forming based on short- and long-term force signal variations

Philipp Niemietz (), Mia J. K. Kornely, Daniel Trauth and Thomas Bergs
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Philipp Niemietz: Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University
Mia J. K. Kornely: Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University
Daniel Trauth: Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University
Thomas Bergs: Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 7, No 15, 2143-2155

Abstract: Abstract Monitoring systems in sheet metal forming cannot rely on direct measurements of the physical condition of interest because the space between the die component and the material is inaccessible. Therefore, in order to gain further insight into the forming or stamping process, sensors must be used to detect auxiliary quantities such as acoustic emission and force that relate to the physical quantities of interest. While it is known that changes in force data are related to physical parameters of the process material, lubricant used, and geometry, the changes in data over large stroke series and their relationship to wear are the subject of this paper. Previously, force data from different wear conditions (artificially introduced into the system and not occurring in an industry-like environment) were used as input for clustering and classifying high and low wear force data. This paper contributes to fill the current research gap by isolating structural properties of data as indicators of wear growth to quantify the wear evolution during ongoing production in industry-like scenarios. The selected methods represent either established methods in sheet metal forming force data analysis, dimensionality reduction for local structure separation or generic feature extraction. The study is conducted on a set of four experiments with each containing about 3000 strokes.

Keywords: Sheet metal forming; Fine-blanking; Condition monitoring; Unsupervised learning; Wear (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-022-01979-0

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