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Polymers
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Machine Learning and Artificial Intelligence for Polymer Processing
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Machine Learning and Artificial Intelligence for Polymer Processing

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Artificial Intelligence in Polymer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4294

Special Issue Editors

Dr. Davide Masato

E-Mail Website
Guest Editor
Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
Interests: polymer processing; product design; modeling and simulation; micro manufacturing; recycling
Special Issues, Collections and Topics in MDPI journals
Dr. Saeed Farahani

E-Mail Website
Guest Editor
School of Mechanical and Automotive Engineering, Clemson University, Greenville, SC 29607, USA
Interests: industry 4.0; industrial IoT; SCADA systems; acquisition and analytics of manufacturing data; physics-regulated AI; data-driven modeling; hybrid manufacturing processes; plastics and composites manufacturing; injection molding; sheet metal forming; specialized tooling
Dr. Peng Gao

E-Mail Website
Guest Editor
Engineering and Design, Western Washington University, Bellingham, WA 98225, USA
Interests: biopolymers; ocean plastics; upcycling & recycling of plastic products; advanced and intelligent polymer processes; sustainable manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative role of machine learning (ML) and artificial intelligence (AI) in the domain of polymer processing technologies and showcase innovative approaches in modeling, simulation, and process control tailored to polymer processing applications. Articles selected for this Special Issue will spotlight the pivotal role of ML and AI in optimizing and advancing aspects of both design and processing. The intersection of AI with design and processing will be a central theme, reflecting the evolving landscape of advanced polymer manufacturing technologies. Authors are encouraged to contribute research that not only advances theoretical frameworks but also rigorously validates them in real-world manufacturing scenarios.

We hope that this Special Issue will serve as a dedicated platform for the sharing of groundbreaking developments, thereby fostering collaboration and catalyzing advancements in the field of advanced polymer processing.

Dr. Davide Masato
Dr. Saeed Farahani
Dr. Peng Gao
Guest Editors

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

  • polymer injection molding
  • polymer processing
  • machine learning
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

23 pages, 1515 KiB  
Article
Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene
by Joshua Krantz, Juliana Licata, Muntaqim Ahmed Raju, Peng Gao, Ruizhe Ma and Davide Masato
Polymers 2025, 17(7), 940; https://doi.org/10.3390/polym17070940 - 30 Mar 2025
Viewed by 386
Abstract
The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents [...] Read more.
The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Polymer Processing)
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19 pages, 7025 KiB  
Article
Energy Consumption Prediction of Injection Molding Process Based on Rolling Learning Informer Model
by Jianfeng Huang, Yi Li, Xinyuan Li, Yucheng Ding, Fenglian Hong and Shitong Peng
Polymers 2024, 16(21), 3097; https://doi.org/10.3390/polym16213097 - 2 Nov 2024
Cited by 1 | Viewed by 1845
Abstract
Accurate energy consumption prediction in the injection molding process is crucial for optimizing energy efficiency in polymer processing. Traditional parameter optimization methods face challenges in achieving optimal energy prediction due to complex energy transmission. In this study, a data-driven approach based on the [...] Read more.
Accurate energy consumption prediction in the injection molding process is crucial for optimizing energy efficiency in polymer processing. Traditional parameter optimization methods face challenges in achieving optimal energy prediction due to complex energy transmission. In this study, a data-driven approach based on the Rolling Learning Informer model is proposed to enhance the accuracy and adaptability of energy consumption forecasting. The Informer model addresses the limitations of long-sequence prediction with sparse attention mechanisms, self-attention distillation, and generative decoder techniques. Rolling learning prediction is incorporated to enable continuous updating of the model to reflect new data trends. Experimental results demonstrate that the RL-Informer model achieves a normalized root mean square error of 0.1301, a root mean square error of 0.0758, a mean absolute error of 0.0562, and a coefficient of determination of 0.9831 in energy consumption forecasting, outperforming other counterpart models like Gated Recurrent Unit, Temporal Convolutional Networks, Long Short-Term Memory, and two variants of the pure Informer models without Rolling Learning. It is of great potential for practical engineering applications. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Polymer Processing)
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22 pages, 2853 KiB  
Article
Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding
by Manuel Wenzel, Sven Robert Raisch, Mauritius Schmitz and Christian Hopmann
Polymers 2024, 16(17), 2465; https://doi.org/10.3390/polym16172465 - 29 Aug 2024
Cited by 3 | Viewed by 1199
Abstract
Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the [...] Read more.
Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Polymer Processing)
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