Simulation of shrinkage and warpage of semi-crystalline thermoplastics

Hopmann, Christian; Borchmann, Nikolai; Spekowius, Marcel; Weber, Michael H.; Schöngart, Maximilian

doi:10.1063/1.4918413

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…For a more detailed description of the simulation of the crystallization see Spina et al [6,8] and Hopmann et. al [1].…”

Section: Crystallizationmentioning

confidence: 98%

“…The main objective of the presented work is to describe the crystallization kinetics of semicrystalline thermoplastics with a multiscale model implemented into the COMSOL software and the in-house developed code SphäroSim (Hopmann et al [1]). This is done by identifying the analytical parameters needed to connect crystallization kinetics with molecular material properties and applying the analytical scheme to the numerical simulation during filling and cooling in an injection moulding process.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Polymer Crystallization with a Multiscale Modeling Approach

Spekowius

Hopmann

2014

KEM

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The main objective of the presented work is to describe the crystallization kinetics of semi-crystalline thermoplastics with a multiscale model implemented into the COMSOL software and the in-house developed code SphäroSim. The filling and cooling simulations, implemented by using the computational fluid dynamics (CFD) and heat transfer (HT) modules of COMSOL, require the simultaneous solution of non-Newtonian multi-phase flow (polymer/air) and thermal fields in non-isothermal condition and transient regime. The simulation results are collected, converted into the OpenSource file format VTK (Visualization Toolkit) and transferred to the SphäroSim code after a matching operation with the COMSOL mesh. The SphäroSim code uses COMSOL results as input data to compute crystallization kinetics, using the COMSOL data as boundary conditions in the microstructure simulation. This allows the time resolved calculation of the crystallization process and a prediction of the final microstructure in the part which can be used in further simulations such as a structural analysis. The analytical parameters needed to connect crystallization kinetics with molecular material properties and applying the analytical scheme to the numerical simulation during filling and cooling in an injection moulding process are identified.

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“…For a more detailed description of the simulation of the crystallization see Spina et al [6,8] and Hopmann et. al [1].…”

Section: Crystallizationmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Analysis of Polymer Crystallization with a Multiscale Modeling Approach

Spekowius

Hopmann

2014

KEM

View full text Add to dashboard Cite

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“…Second, the term “product quality” can be understood in various ways. We can distinguish the three most common groups of the quality indicators of injection molded products: dimensional and weight stability of manufactured parts [ 3 ], surface properties (roughness, sink marks, weld lines, etc.) [ 4 ], and physical properties (mechanical, optical, electrical, etc.)…”

Section: Introductionmentioning

confidence: 99%

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Párizs

Török

Ageyeva

et al. 2023

Sensors

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The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

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“…Product quality in injection molding is quite a complex issue, as quality can be interpreted in various ways [ 11 ]. However, three important groups of quality indicators can be distinguished: (1) the stability of dimensions and weight of the produced parts [ 12 ], surface properties (roughness, sink marks, weld lines, etc.) [ 13 ], and physical properties (mechanical, optical, electrical, etc.)…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction

Párizs

Török

Ageyeva

et al. 2022

Sensors

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One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8–10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.

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Simulation of shrinkage and warpage of semi-crystalline thermoplastics

Cited by 5 publications

References 9 publications

Analysis of Polymer Crystallization with a Multiscale Modeling Approach

Analysis of Polymer Crystallization with a Multiscale Modeling Approach

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction

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