Lens injection moulding condition diagnosis and form error analysis using cavity pressure signals based on response surface methodology

Jung Soo Nam, Dae Seong Baek, Hyoung Han Jo, Jun Yeob Song, Tae Ho Ha, Sangwon Lee

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this article,the condition diagnosis and form error prediction models for lens injection moulding process are developed based on a response surface method by using features extracted from in-process cavity pressure signals. In the lens injection moulding experiments,cavity pressure signals are captured by pressure sensors embedded in a lens mould,and form errors of manufactured lenses are measured afterwards. Then,three features such as filling point,maximum pressure and inflection point pressure are identified from the measured cavity pressure profile,and they are used to formulate the response surface functions for each injection moulding condition. In addition,the response surface functions for the lens form error with the input variables of the above-mentioned three features are also formulated. It is reported that the overall average accuracies for the lens injection moulding condition diagnosis and form error estimation are better than 97% and 80%,respectively,in the actual industrial site.

Original languageEnglish
Pages (from-to)1343-1350
Number of pages8
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume230
Issue number7
DOIs
StatePublished - 1 Jul 2016

Keywords

  • condition diagnosis
  • form error estimation
  • in-process cavity pressure signal
  • Lens injection moulding process
  • response surface methodology

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