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Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 7757

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Prof. Dr. Yuan Yao

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Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
Interests: nondestructive testing data analysis; process data analytics; multivariate analysis; machine learning; process monitoring; soft sensors
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Dr. Stefano Sfarra

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Department of Industrial and Information Engineering and Economics, University of L’Aquila, L'Aquila, Italy
Interests: building heritage; building pathology; infrared thermography; hygrothermal behaviour of buildings; energy efficiency; thermal comfort; numerical modelling; heat transfer; optical metrology; composite materials; NDT
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Dr. Yi Liu

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Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: industrial data intelligence; soft sensor; process systems engineering
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Special Issue Information

Dear Colleagues,

In order to ensure the quality of polymer products, it is usually necessary to measure key process parameters during polymer manufacturing and processing. Some of the parameters relate to the material properties, while some others directly reflect the product quality. In the situations where the key parameters are not measurable in real-time, data-driven statistical or machine learning methods can be adopted to construct soft sensors. In addition, nondestructive testing, including active thermography, ultrasonic testing, etc., is often required to evaluate the end-use products, where both hardware setup and data analytics are important. This Special Issue aims to introduce recent advanced techniques in these fields that can potentially improve polymer manufacturing and processing as well as ensure product quality.

Prof. Dr. Yuan Yao
Dr. Stefano Sfarra
Dr. Yi Liu
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 manufacturing and processing
nondestructive testing
advanced measurement techniques
advanced prediction techniques
advanced testing techniques

Published Papers (6 papers)

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Research

14 pages, 3044 KiB

Open AccessArticle

Configuration of a Simple Method for Different Polyamides 6.9 Recognition by ATR-FTIR Analysis Coupled with Chemometrics

by Maria Laura Tummino, Christoforos Chrimatopoulos, Maddalena Bertolla, Cinzia Tonetti and Vasilios Sakkas

Polymers 2023, 15(15), 3166; https://doi.org/10.3390/polym15153166 - 26 Jul 2023

Cited by 1 | Viewed by 856

Abstract

This study proposes a simple approach for the recognition of polyamide 6.9 samples differing in impurity amounts and viscosities (modulated during the synthesis), which are parameters plausibly variable in polymers’ manufacturing processes. Infrared spectroscopy (ATR-FTIR) was combined with chemometrics, applying statistical methods to experimental data. Both non-supervised and supervised methods have been used (PCA and PLS-DA), and a predictive model that could assess the polyamide type of unknown samples was created. Chemometric tools led to a satisfying degree of discrimination among samples, and the predictive model resulted in a great classification of unknown samples with an accuracy of 88.89%. Traditional physical-chemical characterizations (such as thermal and mechanical tests) showed their limits in the univocal identification of sample types, and additionally, they resulted in time-consuming procedures and specimen destruction. The spectral modifications have been investigated to understand the main signals that are more likely to affect the discrimination process. The proposed hybrid methodology represents a potential support for quality control activities within the production sector, especially when the spectra of compounds with the same nominal composition show almost identical signals. Full article

(This article belongs to the Special Issue Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II)

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14 pages, 3184 KiB

Open AccessArticle

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers Using Constrained Spherical Deconvolution

by Ben Huyge, Jonathan Sanctorum, Ben Jeurissen, Jan De Beenhouwer and Jan Sijbers

Polymers 2023, 15(13), 2887; https://doi.org/10.3390/polym15132887 - 29 Jun 2023

Viewed by 908

Abstract

The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70

^{\circ}

, while CSD performs well down to a crossing angle of 50

^{\circ}

. In general, CSD outperforms the FRT in estimating fiber orientations. Full article

(This article belongs to the Special Issue Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II)

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22 pages, 7325 KiB

Open AccessArticle

Development of an Ultrasonic Method for the Quality Control of Polyethylene Tanks Manufactured Using Rotational Molding Technology

by Vitaliy Tyukanko, Alexandr Demyanenko, Vladislav Semenyuk, Antonina Dyuryagina, Dmitry Alyoshin, Roman Tarunin and Vera Voropaeva

Polymers 2023, 15(10), 2368; https://doi.org/10.3390/polym15102368 - 19 May 2023

Viewed by 1534

Abstract

Tanks made of three different brands of rotational polyethylene (DOW, ELTEX, and M350) with three degrees of sintering (normal sintering (NS), incomplete sintering (ICS), and thermally degraded sintering (TDS)) and three thicknesses (7.5 mm, 8.5 mm, and 9.5 mm) were explored. It was found that the thickness of the walls of the tanks did not have a statistically significant effect on the parameters of the ultrasonic signal (USS). An increase in temperature caused a decrease in the USS parameters. According to the temperature coefficient of stability, the ELTEX brand of plastic can be distinguished (from DOW and M350). The ICS degree of the sintering of the tanks was revealed from a significantly lower amplitude of the bottom signal, compared with NS and TDS degree samples. By analyzing the amplitude of the third harmonic of the ultrasonic signal (β), three degrees of the sintering of containers NS, ICS, and TDS were revealed (with an accuracy of about 95%). Equations β = f(T, PIAT) were derived for each brand of rotational polyethylene (PE), and two-factor nomograms were constructed. Based on the results of this research, a method for the ultrasonic quality control of polyethylene tanks manufactured using rotational molding was developed. Full article

(This article belongs to the Special Issue Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II)

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13 pages, 4984 KiB

Open AccessArticle

Fatigue Performance of a Step-Lap Joint under Tensile Load: A Numerical Study

by Murat Demiral and Ali Mamedov

Polymers 2023, 15(8), 1949; https://doi.org/10.3390/polym15081949 - 19 Apr 2023

Cited by 5 | Viewed by 1272

Abstract

In many technical domains, adhesively bonded joints have been employed extensively. These joints perform poorly against peel stresses despite having good shear characteristics. A step-lap joint (SLJ) is one of the techniques used to reduce the peel stresses at the edges of the overlap area to avoid damages. In these joints, the butted laminations of each layer are successively offset in succeeding layers in the same direction. Bonded joints are subjected to cyclic loadings in addition to static loads. It is difficult to predict their fatigue life accurately; however, this information must be clarified to explain their failure characteristics. To this end, the fatigue response of an adhesively bonded step-lap joint subjected to tensile loading was investigated with the developed finite-element (FE) model. In the joint, toughened type DP 460 and A2024-T3 aluminium alloys were used for the adhesive layer and adherends, respectively. The cohesive zone model with static and fatigue damages were linked to each other and were used to represent the response of the adhesive layer. The model was implemented using an ABAQUS/Standard user-defined UMAT subroutine. Experiments found in the literature served as a basis for validating the numerical model. The fatigue performance of a step-lap joint for various configurations subjected to tensile loading was examined thoroughly. Full article

(This article belongs to the Special Issue Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II)

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20 pages, 7502 KiB

Open AccessArticle

Noise Evaluation of Coated Polymer Gears

by Brigita Polanec, Srečko Glodež and Aleš Belšak

Polymers 2023, 15(3), 783; https://doi.org/10.3390/polym15030783 - 03 Feb 2023

Cited by 2 | Viewed by 1214

Abstract

A comprehensive experimental investigation of the noise evaluation of coated spur polymer gears made of POM was performed in this study. The three Physical Vapour Deposition (PVD) coatings investigated were aluminium (Al), chromium (Cr), and chromium nitrite (CrN). The gears were tested on an in-house-developed testing machine under a torque of 20 Nm and at a rotational speed of 1000 rpm. The noise measurements were performed with the tested gear pair on the testing device with a sound-proof acoustic foam used for the acoustic sound-proof insulation. The sound signal was analysed in time, frequency, and time–frequency domains and typical phenomena were identified in the signal. Experimental results showed that the noise level was higher for polymer gears with different coatings if compared to the polymer gears without coatings. With sound analysis in the time–frequency domain, precise degradation of the coatings could be noticed. In future studies, it would be appropriate to use a new method for signal analysis, e.g., high-order statistics and hybrid technique. Full article

(This article belongs to the Special Issue Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II)

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15 pages, 8291 KiB

Open AccessArticle

Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites

by Kaixin Liu, Fumin Wang, Yuxiang He, Yi Liu, Jianguo Yang and Yuan Yao

Polymers 2023, 15(1), 173; https://doi.org/10.3390/polym15010173 - 29 Dec 2022

Cited by 3 | Viewed by 1235

Abstract

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods. Full article

(This article belongs to the Special Issue Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use II)

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