Predicting part quality early during an injection molding cycle / Lucas Bogedale in INTERNATIONAL POLYMER PROCESSING, Vol. 39, N° 2 (2024)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. 39, N° 2 (2024) . - p. 210-219 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 [article]

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Time series data for process monitoring in injection molding : a quantitative study of the benefits of a high sampling rate / Lucas Bogedale in INTERNATIONAL POLYMER PROCESSING, Vol. 38, N° 2 (2023)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. 38, N° 2 (2023) . - p. 167-174 Titre : Time series data for process monitoring in injection molding : a quantitative study of the benefits of a high sampling rate Type de document : texte imprimé Auteurs : Lucas Bogedale, Auteur ; Alexander Schrodt, Auteur ; Hans-Peter Heim, Auteur Année de publication : 2023 Article en page(s) : p. 167-174 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Apprentissage automatique L'apprentissage automatique (en anglais : machine learning, litt. "apprentissage machine"), apprentissage artificiel ou apprentissage statistique est un champ d'étude de l'intelligence artificielle qui se fonde sur des approches mathématiques et statistiques pour donner aux ordinateurs la capacité d'"apprendre" à partir de données, c'est-à-dire d'améliorer leurs performances à résoudre des tâches sans être explicitement programmés pour chacune. Plus largement, il concerne la conception, l'analyse, l'optimisation, le développement et l'implémentation de telles méthodes. (Wikipedia) Commande de processus Echantillonnage Matières plastiques -- Moulage par injection Pression d'injection Séries chronologiques Index. décimale : 668.4 Plastiques, vinyles Résumé : Process monitoring systems are playing an increasingly important role in reducing production capacity losses in injection molding. Process monitoring and optimization systems are mostly based on processing data of injection molding machine control systems. These data consist of scalar data and time series. This paper introduces a novel approach to modelling injection molding processes using only time series data and evaluates the quantitative influences of varying sampling times on calculation of integral values and model quality. On the basis of the first experiment, it is shown that the sampling rates of these time series have a large influence on information which can be derived from this data (e.g. injection work). These findings provide an assessment of whether the effort is justified for the respective requirements on the accuracy of the injection work and other parameters derived from the time series. In the second experiment, a model is presented which uses only the injection flow and injection pressure profile as input and achieves high coefficients of determination for the prediction of the part weight, despite the absence of mold sensor data and scalar data. It is shown that higher sampling rates of time series results in higher prediction quality of these models. This improves the understanding of the data needed for high quality machine learning models of injection molding processes and enable users to estimate a lower bound for the sample rates of time series for their use cases. Note de contenu : - EXPERIMENTAL SETUP : Injection pressure integral - Analysis - INTERPRETATION - TIME SERIES ML MODEL : Setup - Analysis - Interpretation - Table 1 : Varied parameters DOI : https://doi.org/10.1515/ipp-2022-4258 En ligne : https://drive.google.com/file/d/1DOzkrMWcuxn8WNwiIYALOpw8dzzwlgXj/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=39496 [article]

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