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Korzenowski AL, Simões WL. Quality monitoring by special charts for highly customized production systems. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2018.1513530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- A. L. Korzenowski
- Graduate Degree Program in Production Engineering, Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Brazil
- Graduate Degree Program in Accounting, Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Brazil
| | - W. L. Simões
- Graduate Degree Program in Production Engineering, Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Brazil
- Production Engineering Department, Universidade Luterana do Brasil (ULBRA), Canoas, Brazil
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Wiederhold M, Greipel J, Ottone R, Schmitt R. Clustering of similar processes for the application of statistical process control in small batch and job production. INTERNATIONAL JOURNAL OF METROLOGY AND QUALITY ENGINEERING 2016. [DOI: 10.1051/ijmqe/2016018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Kazmer DO, Westerdale S, Hazen D. A Comparison of Statistical Process Control (SPC) and On-Line Multivariate Analyses (MVA) for Injection Molding. INT POLYM PROC 2013. [DOI: 10.3139/217.2192] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
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.
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Affiliation(s)
- D. O. Kazmer
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - S. Westerdale
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - D. Hazen
- MKS Instruments, Wilmington, MA, USA
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Bessegato LF, Quinino RC, Duczmal LH, Lee Ho L. On-Line Process Control using Attributes with Misclassification Errors: An Economical Design for Short-Run Production. COMMUN STAT-THEOR M 2012. [DOI: 10.1080/03610926.2010.551451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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SNOUSSI ABDELMONEM, GHOURABI MOHAMEDEL, LIMAM MOHAMED. On SPC for Short Run Autocorrelated Data. COMMUN STAT-SIMUL C 2005. [DOI: 10.1081/sac-200047110] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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