| Titre : |
Predicting part quality early during an injection molding cycle |
| Type de document : |
texte imprimé |
| Auteurs : |
Lucas Bogedale, Auteur ; Stephan Doerfel, Auteur ; Alexander Schrodt, Auteur ; Hans-Peter Heim, Auteur |
| Année de publication : |
2024 |
| Article en page(s) : |
p. 210-219 |
| Note générale : |
Bibliogr. |
| Langues : |
Anglais (eng) |
| Catégories : |
Matières plastiques -- Moulage par injection
Prévision technologique
Procédés de fabrication
Qualité -- Contrôle
Surveillance électronique
|
| Index. décimale : |
668.4 Plastiques, vinyles |
| Résumé : |
Data-based process monitoring in injection molding plays an important role in compensating disturbances in the process and the associated impairment of part quality. Selecting appropriate features for a successful online quality prediction based on machine learning methods is crucial. Time series such as the injection pressure and injection flow curve are particularly suitable for this purpose. Predicting quality as early as possible during a cycle has many advantages. In this paper it is shown how the recording length of the time series affects the prediction performance when using machine learning algorithms. For this purpose, two successful molding quality prediction algorithms (k Nearest Neighbors and Ridge Regression) are trained with time series of different lengths on extensive data sets. Their prediction performances for part weight and a geometric dimension are evaluated. The evaluations show that recording time series until the end of a cycle is not necessary to obtain good prediction results. These findings indicate that early reliable quality prediction is possible within a cycle, which speeds up prediction, allows timely part handling at the end of the cycle and provides the basis for automated corrective interventions within the same cycle. |
| Note de contenu : |
- MATERIALS AND METHODS : Datasets
- MACHINE LEARNING METHODOLOGY : Nested cross-validation -
Time series data as features - Targets and evaluation measures - Baselines - Regression algorithms
- RESULTS AND DISCUSSION |
| DOI : |
https://doi.org/10.1515/ipp-2023-4457 |
| En ligne : |
https://drive.google.com/file/d/1yG2E7W9Alesxb5WygB2zkTpIZoQhXsAy/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=40864 |
in INTERNATIONAL POLYMER PROCESSING > Vol. 39, N° 2 (2024) . - p. 210-219
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Predicting part quality early during an injection molding cycle in INTERNATIONAL POLYMER PROCESSING, Vol. 39, N° 2 (2024)
