Visual analysis of quality-related manufacturing data using fractal geometry

Noa Ruschin-Rimini*, Oded Maimon, Roni Romano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Improvingmanufacturing quality is an important challenge in various industrial settings. Data mining methods mostly approach this challenge by examining the effect of operation settings on product quality.We analyze the impact of operational sequences on product quality. For this purpose, we propose a novelmethod for visual analysis and classification of operational sequences. The suggested framework is based on an Iterated Function System (IFS), for producing a fractal representation of manufacturing processes. We demonstrate our method with a software application for visual analysis of quality-related data. The proposed method offers production engineers an effective tool for visual detection of operational sequence patterns influencing product quality, and requires no understanding of mathematical or statistical algorithms. Moreover, it enables to detect faulty operational sequence patterns of any length, without predefining the sequence pattern length. It also enables to visually distinguish between different faulty operational sequence patterns in cases of recurring operations within a production route. Our proposed method provides another significant added value by enabling the visual detection of rare and missing operational sequences per product quality measure.We demonstrate cases in which previous methods fail to provide these capabilities.

Original languageEnglish
Pages (from-to)481-495
Number of pages15
JournalJournal of Intelligent Manufacturing
Issue number3
StatePublished - Jun 2012


  • Chaos game representation
  • Data mining
  • Iterated function system
  • Quality engineering
  • Sequence mining


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