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A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method

[+] Author Affiliations
Dae Seong Baek, Chengjun Li, Jung Soo Nam, Cho Rok Na, Sangwon Lee

Sungkyunkwan University, Suwon, Korea

Myungho Kim, Byungohk Rhee

Ajou University, Suwon, Korea

Paper No. MSEC2014-4058, pp. V001T04A039; 7 pages
doi:10.1115/MSEC2014-4058
From:
  • ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference
  • Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing
  • Detroit, Michigan, USA, June 9–13, 2014
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4580-6
  • Copyright © 2014 by ASME

abstract

The objective of this research is the development of condition diagnosis model for injection molding process based on wavelet packet decomposition (WPD), feature extraction from cavity pressure, nozzle pressure and screw position signals and probability neural network (PNN) method. The node energies from the WPD of cavity and nozzle pressure signals are identified. In addition, five (5), seven (7) and two (2) critical features are extracted from the cavity pressure, nozzle pressure and screw position signals via the new feature extraction algorithm. The node energies and critical features are input to the PNN based condition diagnosis model for the injection modeling process. A series of injection modeling experiments are conducted and their results are used to validate the model. It is demonstrated that the proposed model is applicable to diagnose the injection molding process conditions. In particular, it is also shown that the utilization of cavity pressure and screw position signals in the model can result in higher diagnosis accuracy from the case studies.

Copyright © 2014 by ASME

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