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Polymers
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Artificial Intelligence Techniques for Polymer Processing
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Artificial Intelligence Techniques for Polymer Processing

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Processing and Engineering".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 13966

Special Issue Editor

Prof. Dr. Ming-Shyan Huang

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, 1 University Road, Yanchao, Kaohsiung City 824, Taiwan
Interests: injection molding; system dynamics and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence, the Internet of Things, and 5G technologies has accelerated the development of digital, intelligent, and unmanned manufacturing technologies. It is likely that future technology will be manufactured under an advanced network infrastructure through the deployment and integration of sensing and control technology combined with the integration of virtual and real artificial intelligence and machine learning to establish computation technology so that product design, manufacturing, equipment monitoring and maintenance, production scheduling, and process parameter optimization are integrated and flexible.

Machine learning is an important part of moving towards Industry 4.0. With the huge amount of information available, it is necessary to model manufacturing processes manually at their beginning and after completion. Such processes need to be self-trained by machine learning, so that the continuously input data can adjust the operating conditions more quickly. This is related to technologies such as real-time data pre-processing, feature extraction, and dynamic modification of new data with existing models. As for the application of polymer processing, design defects can be found in the virtual model using virtual and real integration systems to confirm the best time for machine maintenance and perform equipment maintenance in the virtual world. Related technologies include domain knowledge of manufacturing technology, sensing and communication technology, the Internet of Things, cloud computing, artificial intelligence, big data analysis, and digital reality technology.

The aim of this Special Issue is to present the latest research on “Artificial Techniques for Polymer Processing”. We invite researchers to contribute to this issue by submitting related articles and review papers.

Prof. Dr. Ming-Shyan Huang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent quality inspection in polymer processing
  • process parameter optimization in polymer processing
  • intelligent process monitoring and control in polymer processing
  • mold design expert system
  • intelligent control of injection molding machines or other polymer processing devices
  • intelligent manufacturing in polymer processing
  • mold design digitalization
  • cloud computing in polymer processing
  • expert system in defect elimination during polymer processing
  • intelligent injection molding applications or other polymer processing approaches
  • remote control of polymer processing machines
  • advanced sensing in polymer processing
  • IoT in polymer processing
  • intelligent computing in polymer processing
  • machine learning in polymer processing
  • equipment monitoring and maintenance in polymer processing
  • production scheduling in polymer processing
  • intelligent big data analysis in product or mold design.

Published Papers (5 papers)

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Research

16 pages, 3682 KiB  
Article
Comparative Analysis of Machine Learning and Numerical Modeling for Combined Heat Transfer in Polymethylmethacrylate
by Mahsa Dehghan Manshadi, Nima Alafchi, Alireza Tat, Milad Mousavi and Amirhosein Mosavi
Polymers 2022, 14(10), 1996; https://doi.org/10.3390/polym14101996 - 13 May 2022
Cited by 4 | Viewed by 2182
Abstract
This study has compared different methods to predict the simultaneous effects of conductive and radiative heat transfer in a polymethylmethacrylate (PMMA) sample. PMMA is a type of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical [...] Read more.
This study has compared different methods to predict the simultaneous effects of conductive and radiative heat transfer in a polymethylmethacrylate (PMMA) sample. PMMA is a type of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of the governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to obtain data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For the deep neural network (DNN) method, the novel long short-term memory (LSTM) method was introduced to find accurate results in the least processing time compared to the numerical method. A recent study derived the combined heat transfer and temperature profiles for the PMMA sample. Furthermore, the transient temperature profile was validated by another study. A comparison proves the perfect agreement. It shows the temperature gradient in the primary positions, which provides a spectral amount of conductive heat transfer from the PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution, it can be understood that the conductive and radiative heat flux are similar in the case of gradient behavior, but the amount is also twice as high approximately. Hence, total heat flux has a constant value in an approximated steady-state condition. In addition to analyzing their composition, the receiver operating characteristic (ROC) curve and confusion matrix were implemented to evaluate the algorithm’s performance. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques for Polymer Processing)
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22 pages, 4000 KiB  
Article
Inline Quality Control through Optical Deep Learning-Based Porosity Determination for Powder Bed Fusion of Polymers
by Samuel Schlicht, Andreas Jaksch and Dietmar Drummer
Polymers 2022, 14(5), 885; https://doi.org/10.3390/polym14050885 - 23 Feb 2022
Cited by 4 | Viewed by 3793
Abstract
Powder bed fusion of thermoplastic polymers is a powder based additive manufacturing process that allows for manufacturing individualized components with high geometric freedom. Despite achieving higher mechanical properties compared to other additive manufacturing processes, statistical variations in part properties and the occurrence of [...] Read more.
Powder bed fusion of thermoplastic polymers is a powder based additive manufacturing process that allows for manufacturing individualized components with high geometric freedom. Despite achieving higher mechanical properties compared to other additive manufacturing processes, statistical variations in part properties and the occurrence of defects cannot be avoided systematically. In this paper, a novel method for the inline assessment of part porosity is proposed in order to detect and to compensate for inherent limitations in the reproducibility of manufactured parts. The proposed approach is based on monitoring the parameter-specific decay of the optical melt pool radiance during the melting process, influenced by a time dependency of optical scattering within the melt pool. The underlying methodology compromises the regression of the time-dependent optical melt pool properties, assessed in visible light using conventional camera technology, and the resulting part properties by means of artificial neural networks. By applying deep residual neural networks for correlating time-resolved optical process properties and the corresponding part porosity, an inline assessment of the spatially resolved part porosity can be achieved. The authors demonstrate the suitability of the proposed approach for the inline porosity assessment of varying part geometries, processing parameters, and material aging states, using Polyamide 12. Consequently, the approach represents a methodological foundation for novel monitoring solutions, the enhanced understanding of parameter–material interactions and the inline-development of novel material systems in powder bed fusion of polymers. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques for Polymer Processing)
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22 pages, 4925 KiB  
Article
Integrating Taguchi Method and Gray Relational Analysis for Auto Locks by Using Multiobjective Design in Computer-Aided Engineering
by Wei-Tai Huang, Zi-Yun Tasi, Wen-Hsien Ho and Jyh-Horng Chou
Polymers 2022, 14(3), 644; https://doi.org/10.3390/polym14030644 - 08 Feb 2022
Cited by 10 | Viewed by 1922
Abstract
In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of [...] Read more.
In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of the plastic part is caused by the nonuniform product temperature distribution in the manufacturing process. Furthermore, improper parameter design leads to substantial warpage and deformation. The Taguchi robust design method and gray correlation analysis were used to optimize the process parameters. Multiobjective quality analysis was performed for achieving a uniform temperature distribution and reducing the warpage deformation to obtain the optimal injection molding process parameters. Subsequently, three water cooling system designs—original cooling, U-shaped cooling, and conformal cooling—were tested to modify the temperature distribution and reduce the warpage. Taguchi gray correlation analysis revealed that the main influencing parameter was the mold temperature followed by the holding pressure. Moreover, the results indicated that the conformal cooling system improved the average temperature distribution. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques for Polymer Processing)
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13 pages, 2619 KiB  
Article
Research on Quality Characterization Method of Micro-Injection Products Based on Cavity Pressure
by Quan Wang, Xiaomei Zhao, Jianpeng Zhang, Ping Zhang, Xinwei Wang, Chaofeng Yang, Jinrong Wang and Zhenghuan Wu
Polymers 2021, 13(16), 2755; https://doi.org/10.3390/polym13162755 - 17 Aug 2021
Cited by 6 | Viewed by 1942
Abstract
The cavity pressure in the injection molding process is closely related to the quality of the molded products, and is used for process monitoring and control, to upgrade the quality of the molded products. The experimental platform was built to carry out the [...] Read more.
The cavity pressure in the injection molding process is closely related to the quality of the molded products, and is used for process monitoring and control, to upgrade the quality of the molded products. The experimental platform was built to carry out the cavity pressure experiment with a micro spline injection mold in the paper. The process parameters were changed, such as V/P switchover, mold temperature, melt temperature, packing pressure, and injection rate, in order to analyze the influence of the process parameters on the product weight. The peak cavity pressure and area under the pressure curve were the two attributes utilized in investigating the correlation between cavity pressure and part weight. The experimental results show that the later switchover allowed the injection to proceed longer and produce a heavier tensile specimen. By comparing different cavity pressure curves, the general shapes of the curves were able to indicate different types of shortage produced. When the V/P switchover position is 10 mm, the coefficient of determination (R2 value) of part weight, for the peak cavity pressure and area under the curve, were 0.7706 and 0.8565, respectively. This showed that the area under the curve appeared to be a better process and quality indicator than the peak cavity pressure. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques for Polymer Processing)
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15 pages, 2646 KiB  
Article
Pressure Equilibrium Time of a Cyclic-Olefin Copolymer
by Benedikt Roth and Dietmar Drummer
Polymers 2021, 13(14), 2309; https://doi.org/10.3390/polym13142309 - 14 Jul 2021
Cited by 3 | Viewed by 1902
Abstract
Integrative simulation techniques for predicting component properties, based on the conditions during processing, are becoming increasingly important. The calculation of orientations in injection molding, which, in addition to mechanical and optical properties, also affect the thermal shrinkage behavior, are modeled on the basis [...] Read more.
Integrative simulation techniques for predicting component properties, based on the conditions during processing, are becoming increasingly important. The calculation of orientations in injection molding, which, in addition to mechanical and optical properties, also affect the thermal shrinkage behavior, are modeled on the basis of measurements that cannot take into account the pressure driven flow processes, which cause the orientations during the holding pressure phase. Previous investigations with a high-pressure capillary rheometer (HPC) and closed counter pressure chamber (CPC) showed the significant effect of a dynamically applied pressure on the flow behavior, depending on the temperature and the underlying compression rate. At a constant compression rate, an effective pressure difference between the measuring chamber and the CPC was observed, which resulted in a stop of flow through the capillary referred to as dynamic compression induced solidification. In order to extend the material understanding to the moment after dynamic solidification, an equilibrium time, which is needed until the pressure signals equalize, was evaluated and investigated in terms of a pressure, temperature and a possible compression rate dependency in this study. The findings show an exponential increase of the determined equilibrium time as a function of the holding pressure level and a decrease of the equilibrium time with increasing temperature. In case of supercritical compression in the area of a dynamic solidification, a compression rate dependency of the determined equilibrium times is also found. The measurement results show a temperature-invariant behavior, which allows the derivation of a master curve, according to the superposition principle, to calculate the pressure equilibrium time as a function of the holding pressure and the temperature. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques for Polymer Processing)
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