Application of neural network and evolutionary algorithm in operation sequencing using datum hierarchy trees

S. C. Fok, G. Thimm, G. Britton

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In manufacturing, the determination of a (near) optimal sequence of machining operations to create a part is a non-trivial task. The paper presents the use of datum hierarchy trees in operation sequencing to ensure the finished part satisfies the design tolerances. Based on this technique, a framework is proposed to automate not only the retrieval of relevant plans for evolving the datum hierarchy tree of a new part, but also the optimisation of the operation sequence subjected to constraints on group cell layout, cut sequence, and machine tolerances. The retrieval process is based on the classification of parts using a back propagation neural network while the operation sequence is optimised using an evolutionary algorithm. Results showed that both geometrical and part features may be needed to assist the categorisation of the parts investigated. The results from a case study using industry parts from an aerospace company revealed the potential practical value of the proposed approach in deriving operation sequence for minimal machine and datum changes, both including and excluding manufacturing sequence constraints imposed by group cell layout.

Original languageEnglish
Pages (from-to)114-130
Number of pages17
JournalInternational Journal of Machining and Machinability of Materials
Issue number1-2
Publication statusPublished - 2010
Externally publishedYes


  • Datum-hierarchy tree
  • Evolutionary algorithm
  • Machining
  • Neural network
  • Operational sequencing
  • Tolerance charting

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering
  • Mechanics of Materials

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