Artificial Intelligence Enhanced Design of Polymer Materials and Manufacturing

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 10105

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Guest Editor
School of Aerospace and Mechanical Engineering, University of Oklahoma, 660 Parrington Oval, Norman, OK 73019, USA
Interests: additive manufacturing; advanced polymers and multifunctional composites; intelligent sensors and structures; nondestructive inspection; structural health monitoring and prognostics
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Hudson College of Public Health, University of Oklahoma Health Science Center, 1100 N Lindsay Ave, Oklahoma City, OK 73104, USA
Interests: industrial hygiene; aerosol; nanoparticles; machine learning; air quality modeling

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Guest Editor
Raven 3D LLC, Norman, OK 73069-5723, USA
Interests: additive manufacturing; composite materials; nanocomposites

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) and machine learning (ML) technologies have shown great potential to transform the design and manufacturing of polymers, composites, and nanocomposites through advanced analytics tools. This technology trend leads to the digitalization of materials’ design and manufacturing process and challenges researchers and engineers to reconsider and reevaluate their current technologies and future strategic directions in the new era known as Smart Materials and Manufacturing for Industry 4.0. It has been well recognized that AI/ML can reap substantial time and cost savings in the development of new polymers by identifying new promising molecules whose physicochemical properties meet arbitrary given requirements. Systematic integration of AI/ML-enabled material design, synthesis, characterization, and application can revolutionize the polymer and composite industry, leading to the rapid development of new material systems for broad applications in aerospace, mechanical, biomedical, civil engineering. Additionally, AI/ML technologies have the potential to significantly improve current advanced manufacturing methods, such as stereolithography, direct ink writing, fused deposition modeling, and selective laser sintering. This Special Issue entitled “Artificial Intelligence Enhanced Design of Materials and Manufacturing” provides a platform for the polymers, composites, AI/ML, and advanced manufacturing communities to collaborate and present their cutting-edge breakthroughs in fundamental and applied science relevant to the field of material design, and advanced manufacturing for novel polymers, composites, and nanomaterials. Topics of interests include but are not limited to the following: 

  • ML-enhanced design of polymers, composites, and nanocomposites;
  • AI/ML driven advanced manufacturing;
  • material–process–property–performance of novel materials;
  • novel materials and process for Industrial 4.0;
  • AI/ML-enabled modeling and simulation;
  • additive manufacturing with advanced sensors, big data, and control. 

Dr. Yingtao Liu
Dr. Changjie Cai
Dr. Blake Herren
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • big data
  • deep learning
  • industrial 4.0
  • design of materials
  • polymer synthesis and characterization
  • multiscale testing
  • additive manufacturing
  • hybrid manufacturing
  • cyber manufacturing
  • materials–process–property relationships

Published Papers (5 papers)

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Research

16 pages, 5249 KiB  
Article
Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization
by Jelena Fiosina, Philipp Sievers, Gavaskar Kanagaraj, Marco Drache and Sabine Beuermann
Polymers 2024, 16(7), 945; https://doi.org/10.3390/polym16070945 - 29 Mar 2024
Viewed by 616
Abstract
Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal [...] Read more.
Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives. Full article
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13 pages, 5026 KiB  
Article
Photocurable Polymer-Based 3D Printing: Advanced Flexible Strain Sensors for Human Kinematics Monitoring
by Christopher Billings, Ridwan Siddique and Yingtao Liu
Polymers 2023, 15(20), 4170; https://doi.org/10.3390/polym15204170 - 20 Oct 2023
Cited by 1 | Viewed by 1242
Abstract
Vat photopolymerization-based additive manufacturing (AM) is critical in improving solutions for wearable sensors. The ability to add nanoparticles to increase the polymer resin’s mechanical, electrical, and chemical properties creates a strong proposition for investigating custom nanocomposites for the medical field. This work uses [...] Read more.
Vat photopolymerization-based additive manufacturing (AM) is critical in improving solutions for wearable sensors. The ability to add nanoparticles to increase the polymer resin’s mechanical, electrical, and chemical properties creates a strong proposition for investigating custom nanocomposites for the medical field. This work uses a low-cost biocompatible polymer resin enhanced with multi-walled carbon nanotubes (MWCNTs), and a digital light processing-based AM system to develop accurate strain sensors. These sensors demonstrate the ability to carry a 244% maximum strain while lasting hundreds of cycles without degradation at lower strain ranges. In addition, the printing process allows for detailed prints to be accomplished at a sub-30 micron spatial resolution while also assisting alignment of the MWCNTs in the printing plane. Moreover, high-magnification imagery demonstrates uniform MWCNT dispersion by utilizing planetary shear mixing and identifying MWCNT pullout at fracture locations. Finally, the proposed nanocomposite is used to print customized and wearable strain sensors for finger motion monitoring and can detect different amounts of flexion and extension. The 3D printed nanocomposite sensors demonstrate characteristics that make it a strong candidate for the applications of human kinematics monitoring and sensing. Full article
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23 pages, 3622 KiB  
Article
Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
by Nimra Munir, Ross McMorrow, Konrad Mulrennan, Darren Whitaker, Seán McLoone, Minna Kellomäki, Elina Talvitie, Inari Lyyra and Marion McAfee
Polymers 2023, 15(17), 3566; https://doi.org/10.3390/polym15173566 - 28 Aug 2023
Cited by 4 | Viewed by 1497
Abstract
This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to [...] Read more.
This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially ‘black-box’ in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings. Full article
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15 pages, 11067 KiB  
Article
Funnel-Shaped Floating Vessel Oil Skimmer with Joule Heating Sorption Functionality
by Blake Herren, Mrinal C. Saha, M. Cengiz Altan and Yingtao Liu
Polymers 2022, 14(11), 2269; https://doi.org/10.3390/polym14112269 - 2 Jun 2022
Cited by 7 | Viewed by 2834
Abstract
Floating vessel-type oil collecting devices based on sorbent materials present potential solutions to oil spill cleanup that require a massive amount of sorbent material and manual labor. Additionally, continuous oil extraction from these devices presents opportunities for highly energy-efficient oil skimmers that use [...] Read more.
Floating vessel-type oil collecting devices based on sorbent materials present potential solutions to oil spill cleanup that require a massive amount of sorbent material and manual labor. Additionally, continuous oil extraction from these devices presents opportunities for highly energy-efficient oil skimmers that use gravity as the oil/water separation mechanism. Herein, a sorbent-based oil skimmer (SOS) is developed with a novel funnel-shaped sorbent and vessel design for efficient and continuous extraction of various oils from the water surface. A carbon black (CB) embedded polydimethylsiloxane (PDMS) sponge material is characterized and used as the sorbent in the SOS. The nanocomposite sponge formulation is optimized for high reusability, hydrophobicity, and rapid oil absorption. Joule heating functionality of the sponge is also explored to rapidly absorb highly viscous oils that are a significant challenge for oil spill cleanup. The optimized sponge material with the highest porosity and 15 wt% CB loading is tested in the SOS for large-scale oil spill extraction tests and shows effective cleaning of oil spilled on the water surface. The SOS demonstrates a high maximum extraction rate of 200 mL/min for gasoline and maintains a high extraction rate performance upon reuse when the sponge funnel is cleaned and dried. Full article
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24 pages, 2399 KiB  
Article
Accelerated Discovery of the Polymer Blends for Cartilage Repair through Data-Mining Tools and Machine-Learning Algorithm
by Anusha Mairpady, Abdel-Hamid I. Mourad and Mohammad Sayem Mozumder
Polymers 2022, 14(9), 1802; https://doi.org/10.3390/polym14091802 - 28 Apr 2022
Cited by 5 | Viewed by 2188
Abstract
In designing successful cartilage substitutes, the selection of scaffold materials plays a central role, among several other important factors. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally it requires [...] Read more.
In designing successful cartilage substitutes, the selection of scaffold materials plays a central role, among several other important factors. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally it requires numerous trials. Moreover, it is humanly impossible to go through the huge library of literature available on the potential polymer(s) and to correlate the physical, mechanical, and biological properties that might be suitable for cartilage tissue engineering. Hence, the objective of this study is to implement an inverse design approach to predict the best polymer(s)/blend(s) for cartilage repair by using a machine-learning algorithm (i.e., multinomial logistic regression (MNLR)). Initially, a systematic bibliometric analysis on cartilage repair has been performed by using the bibliometrix package in the R program. Then, the database was created by extracting the mechanical properties of the most frequently used polymers/blends from the PoLyInfo library by using data-mining tools. Then, an MNLR algorithm was run by using the mechanical properties of the polymers, which are similar to the cartilages, as the input and the polymer(s)/blends as the predicted output. The MNLR algorithm used in this study predicts polyethylene/polyethylene-graftpoly(maleic anhydride) blend as the best candidate for cartilage repair. Full article
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