Analysis of Quality Issues in Production With Multi-view Coordination Assets

Sebastian Kropatschek*, Thorsten Steuer*, Elmar Kiesling, Kristof Meixner, Iman Ayatollahi*, Patrik Sommer, Stefan Biffl*

*Corresponding author for this work

Publication: Scientific journalJournal articlepeer-review

Abstract

The diffusion of the Industry 4.0 paradigm has led to a proliferation of data that is generated by production assets on the shop floor. This data opens up new opportunities for the analysis of quality issues, but it also makes identifying, selecting, and correctly interpreting data all the more critical. This involves a multitude of domain experts that design, operate and maintain production equipment. Major challenges they face in this context are (i) to map and integrate their domain knowledge on potential failure modes and effects, products, processes and production assets; and (ii) to coordinate their actions to systematically investigate and address the most important issues first. To address these challenges, this paper introduces the FMEA-linked-to-PPR Asset Issue Analysis (FPI) Model, a multi-view coordination asset, to guide quality issue analyses. The model integrates cross-domain knowledge and facilitates tracking the investigation state of quality analyses in teams of domain experts. A preliminary evaluation on a real-world use case indicates the FPI model to facilitate effective cross-domain analytic processes and the efficient identification of potential causes for quality issues.

Original languageEnglish
Pages (from-to)2938-2943
Number of pages6
JournalIFAC-PapersOnLine
Volume55
Issue number10
DOIs
Publication statusPublished - 2022
Event10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 - Nantes, France
Duration: 22 Jun 202224 Jun 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Keywords

  • Knowledge management in production
  • Monitoring of product quality and control performance
  • Multi-view modeling of manufacturing operations
  • Quality management

Cite this