Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data

Jose V. Abellan-Nebot, Jian Liu, F. Romero Subiron

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Process design has been intensively studied to reduce dimensional variability of products produced in multi-station manufacturing processes (MMPs). Most of the existing studies focus on predicting variation propagation and evaluating process robustness. However, these studies overlook the potential use of historical shop-floor quality data of existing MMPs in order to extract the actual manufacturing operation capabilities from each station, and then, to evaluate more accurately the expected dimensional variability of new candidate process plans. This paper proposes a methodology to improve process plan selection based on three components: (i) based on historical shop floor data, an inference on the process capabilities of the stations in an existing MMP, which will be used to produce the new product; (ii) a sensitivity analysis of candidate process plans to identify critical fixtures and manufacturing stations/operations; and (iii) an optimal selection of candidate process plans. A case study is presented to demonstrate the effectiveness of the methodology.

Original languageEnglish (US)
Pages (from-to)70-82
Number of pages13
JournalJournal of Manufacturing Systems
Volume30
Issue number2
DOIs
StatePublished - Apr 2011

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Sensitivity analysis
Process design

Keywords

  • Historical data
  • Process capability
  • Process planning
  • Sensitivity analysis
  • Variation propagation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Hardware and Architecture
  • Software

Cite this

Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data. / Abellan-Nebot, Jose V.; Liu, Jian; Romero Subiron, F.

In: Journal of Manufacturing Systems, Vol. 30, No. 2, 04.2011, p. 70-82.

Research output: Contribution to journalArticle

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