Application of multivariate statistical analysis for CNC milling of large Ti-6Al-4V components

L. Asensio Dominguez, A. Shokrani, J. M. Flynn, V. Dhokia, S. T. Newman

Research output: Contribution to journalConference articlepeer-review

8 Citations (SciVal)


Large-scale aircraft components made from difficult-to-machine materials such as titanium alloy Ti-6Al-4V, present a challenge to machining. Typical aircraft component features such as high thin walls or deep pockets, cause significant problems with cutter vibrations and chatter as a consequence of material properties. Premature failure of the cutting tools and damage of the machined surface can be minimised by selecting an appropriate machining strategy with optimised operational parameters. The aim of the research reported in this paper is to perform an exploratory case study through the use of a multivariate analysis technique known as Principal Component Analysis to graphically visualise the relationship between the variables of cutting speed, feed rate, radial depth of cut, shank length, milled surface quality and tool wear during peripheral milling of a workpiece made from annealed Ti-6Al-4V. The results of the research identify that the relationships between these variables are validated with that found in the literature. It is shown that there is significant potential in employing Principal Component Analysis as a data mining technique during computer numerical control (CNC) machining processes especially when large data sets are available.

Original languageEnglish
Pages (from-to)800-807
Number of pages8
JournalProcedia Manufacturing
Publication statusPublished - 1 Jan 2019
Event29th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2019 - Limerick, Ireland
Duration: 24 Jun 201928 Jun 2019


  • Data mining
  • Long shank
  • Peripheral milling
  • Principal Component Analysis
  • Ti-6Al-4V

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


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