A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing

Moschos Papananias*, Thomas E. McLeay, Mahdi Mahfouf, Visakan Kadirkamanathan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Manufacturing is usually performed as a sequence of operations such as forming, machining, inspection, and assembly. A new challenge in manufacturing is to move towards Industry 4.0 (the fourth Industrial revolution) concerning the full integration of machines and production systems with machine learning methods to enable for intelligent multistage manufacturing. This paper discusses Multistage Manufacturing Processes (MMPs) and develops a probabilistic model based on Bayesian linear regression to estimate the results of final inspection associated with comparative coordinate measurement given in-process measured coordinates. The results of two case studies for flatness tolerance evaluation demonstrate the effectiveness of the probabilistic model which aims at being part of a larger metrology informatics system to be developed for predictive analytics and agent-based advanced control in multistage manufacturing. This solution relying on accurate models can minimise post-process inspection in mass production with independent measurements.

Original languageEnglish
Pages (from-to)35-47
Number of pages13
JournalComputers in Industry
Volume105
DOIs
Publication statusPublished - 22 Feb 2019

Keywords

  • ANOVA
  • Bayesian inference
  • measurement uncertainty
  • metrology informatics
  • multistage manufacturing process (MMP)
  • regression
  • UKRI
  • EPSRC
  • EP/P006930/1

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