Residual stress assessment in inconel 718 machining through wavelet sensor signal analysis and sensor fusion pattern recognition

T. Segreto, S. Karam, A. Simeone, R. Teti

Research output: Contribution to journalConference articlepeer-review

20 Citations (Scopus)

Abstract

On-line residual stress assessment in turning of Inconel 718 was carried out through multiple sensor monitoring based on cutting force, acoustic emission and vibration signals acquisition and analysis. The detected sensor signals were processed by the wavelet packet transform technique to extract statistical features from the packet coefficients for the construction of wavelet feature vectors. The latter were used for sensor fusion pattern recognition through neural network data processing grounded on X-ray diffraction residual stress measurements on the turned part surface. The scope of the sensory data fusion approach was to achieve a robust scheme for multi-sensor monitoring decision making on machined surface integrity in terms of residual stress level acceptability.

Original languageEnglish
Pages (from-to)103-108
Number of pages6
JournalProcedia CIRP
Volume9
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2nd CIRP Global Web Conference on Beyond Modern Manufacturing: Technology for the Factories of the Future, CIRPe 2013 - Patras, Greece
Duration: 11 Jun 201312 Jun 2013

Keywords

  • Neural networks
  • Residual stress
  • Sensor fusion
  • Sensor monitoring
  • Superalloy machining
  • Wavelet packet transform

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