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A Comparison of Statistical Process Control (SPC) and On-Line Multivariate Analyses (MVA) for Injection Molding

  • D. O. Kazmer , S. Westerdale and D. Hazen
Published/Copyright: April 6, 2013
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International Polymer Processing
From the journal International Polymer Processing Volume 23 Issue 5

Abstract

Manufacturing process automation is often impeded by limitations related to automatic quality assurance. Many plastics manufacturers use univariate statistical process control (SPC) for quality control by charting the critical process states relative to defined control limits. Alternatively, principal component analysis (PCA) and projection to latent stuctures (PLS) are multivariate methods that measure the process variance by the distance to the model (DModX) and the Hotelling t-squared (T2) values. A methodology for robust model development is described to perturb the manufacturing process for process characterization based on a design of experiments; best subset analysis is used to provide an optimal set of regressors for univariate SPC. Four different statistical models were derived from the same data set for a highly instrumented injection molding process. The performance of these models was then assessed with respect to fault diagnosis and defect identification when the molding process was subjected to twelve common process faults. Across two hundred molding cycles, the univariate SPC models correctly diagnosed five of the twelve process faults with one false positive, detecting only eighteen of twenty four defective products while indicating two false positives. With the same molding cycles, PCA and PLS provided nearly identical performance by correctly diagnosing ten of the twelve process faults and detecting twenty three of the twenty four defective products; PCA indicated two false positives while PLS indicated only one false positive.

∗ Mail address: David O. Kazmer, Dept. Plastics Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854. E-mail:

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Received: 2008-5-28
Accepted: 2008-7-9
Published Online: 2013-04-06
Published in Print: 2008-11-01

© 2008, Carl Hanser Verlag, Munich

Articles in the same Issue

  1. Contents
  2. Contents
  3. Editorial
  4. Robert Simha, 04.08.1912–05.06.2008
  5. Invited Paper
  6. Crystallization of PLA/Thermoplastic Starch Blends
  7. Regular Contributed Articles
  8. Study on Warpage Behavior and Filler Orientation during Injection Molding
  9. Investigation of the Effects of Formulation and Processing Parameters on Properties of PA 6 Nanocomposites using Taguchi Method of Experimental Design
  10. A Two-level Decomposition Method for Cooling System Optimization in Injection Molding
  11. A Comparison of Statistical Process Control (SPC) and On-Line Multivariate Analyses (MVA) for Injection Molding
  12. PPS News
  13. PPS News
  14. Seikei-Kakou Abstracts
  15. Seikei-Kakou Abstracts
https://doi.org/10.3139/217.2192

Articles in the same Issue

  1. Contents
  2. Contents
  3. Editorial
  4. Robert Simha, 04.08.1912–05.06.2008
  5. Invited Paper
  6. Crystallization of PLA/Thermoplastic Starch Blends
  7. Regular Contributed Articles
  8. Study on Warpage Behavior and Filler Orientation during Injection Molding
  9. Investigation of the Effects of Formulation and Processing Parameters on Properties of PA 6 Nanocomposites using Taguchi Method of Experimental Design
  10. A Two-level Decomposition Method for Cooling System Optimization in Injection Molding
  11. A Comparison of Statistical Process Control (SPC) and On-Line Multivariate Analyses (MVA) for Injection Molding
  12. PPS News
  13. PPS News
  14. Seikei-Kakou Abstracts
  15. Seikei-Kakou Abstracts
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