Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

K. Patra, A. K. Jha, T. Szalay, J. Ranjan, L. Monostori

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.

Original languageEnglish
Pages (from-to)279-291
Number of pages13
JournalPrecision Engineering
Volume48
DOIs
Publication statusPublished - Apr 1 2017

Fingerprint

Condition monitoring
Drilling
Neural networks
Machining
Wear of materials
Tool steel
Process monitoring
Electric fuses
Monitoring

Keywords

  • Artificial neural network
  • Micro-drilling
  • Peck drilling
  • Regression analysis
  • Thrust force
  • Tool breakage
  • Tool wear

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. / Patra, K.; Jha, A. K.; Szalay, T.; Ranjan, J.; Monostori, L.

In: Precision Engineering, Vol. 48, 01.04.2017, p. 279-291.

Research output: Contribution to journalArticle

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