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Assessment of quality predictions achieved with machine learning using established measurement process capability procedures in manufacturing

Authors
  • Schorr, Sebastian1
  • Bähre, Dirk2
  • Schütze, Andreas3
  • 1 Bosch Rexroth AG, Bexbacher Straße 72 , (Germany)
  • 2 Lehrstuhl für Fertigungstechnik LFT, Germany , (Germany)
  • 3 Lehrstuhl für Messtechnik LMT, Germany , (Germany)
Type
Published Article
Journal
tm - Technisches Messen
Publisher
De Gruyter Oldenbourg
Publication Date
Feb 26, 2022
Volume
89
Issue
4
Pages
240–252
Identifiers
DOI: 10.1515/teme-2021-0125
Source
De Gruyter
Keywords
Disciplines
  • Beiträge
License
Yellow

Abstract

The increasing amount of available process data from machining and other manufacturing processes together with machine learning methods provide new possibilities for quality control and condition monitoring. A prediction of the workpiece quality in an early machining stage can be used to alter current quality control strategies and could lead to savings in terms of time, cost and resources. However, most methods are tested under controlled lab conditions and few implementations in real manufacturing processes have been reported yet. The main reason for this slow uptake of this promising technology is the need to prove the capability of a machine learning method for quality prediction before it can be applied in serial production and supplement current quality control methods. This article introduces and compares approaches from the fields of machine learning and quality management in order to assess predictions. The comparison and adaption of the two approaches is carried out for an industrial use case at Bosch Rexroth AG where the diameter and the roundness of bores are predicted with machine learning based on process data.

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