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Polymers
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Advances in Polymers Processing and Injection Molding
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Advances in Polymers Processing and Injection Molding

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

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 48237

Special Issue Editors

Prof. Dr. Byungohk Rhee

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Ajou University, Suwon, Gyeonggi-do, Korea
Interests: process monitoring and control for injection molding; rheological chracterization of polymeric materials and PIM (powder injection molding) feedstocks; mold and process design optimization
Prof. Dr. Lih-Sheng Turng

E-Mail Website
Guest Editor
Department of mechanical engineering, University of Wisconsin–Madison, 330 North Orchard Street, Madison, WI 53715, USA
Interests: injection molding and innovative plastics manufacturing processes; bio-based polymers and tissue engineering scaffolds; microcellular injection molding
Prof. Dr. Christian Hopmann

E-Mail Website
Guest Editor
Institute for Plastics Processing at RWTH Aachen University, Aachen, Germany
Interests: plastics processing and industry 4.0; modelling and simulation of processes and plstics parts; additive manufacturing
Dr. Jinsu Gim

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: injection molding; polymer processing; plastics engineering; plastics processing; rheological characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Injection molding is one of the most important polymer processing methods, accounting for approximately one-third of all thermoplastics processed. It has been ranked No. 1 in terms of total product value, number of machines built and sold, and overall employments. There have been numerous papers published in the past that extensively covered topics such as material developments, process control and optimization, part and mold designs, tooling and part quality, special injection molding processes, and computer modeling and simulation, just to name a few. However, due to growing environmental concerns, increasing use of computer tools, emerging application of data science, and new developments in materials and additive manufacturing, the technological landscape of injection molding has dramatically changed. This special issue of Polymer aims to solicit and publish original, high-quality papers that cover a broad range of topics ranging from, but not limited to, materials, mold technology, special injection machines, CAE technology, intelligent injection molding technology, monitoring technology, innovative molding processes, rheology, and sustainability, as listed in the keywords below.

Papers that cover latest developments in network-based production, artificial intelligence and/or machine learning, bioplastics, and additive manufacturing are also welcome. This special issue strives to disseminate important technological developments and knowledge discovery to current and future generations of scientists and engineers on the latest injection molding technology, processes, and materials, which have not been comprehensively covered in prior publications or public domain.

Prof. Dr. Byungohk Rhee
Prof. Dr. Lih-Sheng Turng
Prof. Dr. Christian Hopmann
Dr. Jinsu Gim
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

  • Materials
    • Long fiber thermoplastics
    • Nano composites
    • Bioplastic materials
    • Recycled materials
  • Mold technology
    • Thermoplastic/Thermosetting integrated mold
    • Quick change mold
    • Conformal cooling channel design
    • RHCM or variothermal mold
    • Optimization of mold cooling channels
    • Design optimization of sequential valve gating for HRS
  • Intelligent injection machine
    • Ultra-fast injection machine
    • Energy saving machine
    • Extrusion-injection molding machine for LFT
  • CAE technology
    • Filling simulation for micro featured surface
    • CAE with viscoelasticity model
  • Intelligent injection molding technology
    • Artificial intelligence for injection molding
    • Intelligent design of injection molds
    • In-situ detection of molding defects during the process
  • Monitoring technology
    • Smart and integrated in-mold sensors
    • Sensor fusion for injection molding
  • Innovative molding process
    • Multi-component (thermoplastic, thermoset, metal, ceramic) molding
    • Multi-layer molding
    • Micro molding
    • Foam injection molding
  • Rheology
    • Rheological characterization of polymer materials
    • Rheology in injection molding
  • Sustainability
    • Molding process using recycled polymers
    • Molding process using bioplastics

Published Papers (18 papers)

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Research

21 pages, 50253 KiB  
Article
Development of a Rapid Tool for Metal Injection Molding Using Aluminum-Filled Epoxy Resins
by Chil-Chyuan Kuo and Xin-Yu Pan
Polymers 2023, 15(17), 3513; https://0-doi-org.brum.beds.ac.uk/10.3390/polym15173513 - 23 Aug 2023
Cited by 3 | Viewed by 901
Abstract
Metal injection molding (MIM) is a near net-shape manufacturing process combining conventional plastic injection molding and powder metallurgy. Two kinds of injections molds for MIM were developed using conventional mold steel and aluminum (Al)-filled epoxy resins in this study. The characteristics of the [...] Read more.
Metal injection molding (MIM) is a near net-shape manufacturing process combining conventional plastic injection molding and powder metallurgy. Two kinds of injections molds for MIM were developed using conventional mold steel and aluminum (Al)-filled epoxy resins in this study. The characteristics of the mold made by rapid tooling technology (RTT) were evaluated and compared with that of the fabricated conventional machining method through the MIM process. It was found that the service life of the injection mold fabricated by Al-filled epoxy resin is about 1300 molding cycles with the average surface roughness of 158 nm. The mold service life of the injection mold fabricated by Al-filled epoxy resin is about 1.3% that of the conventional mold steel. The reduction in manufacturing cost of an injection mold made by Al-filled epoxy resin is about 30.4% compared with that of the fabricated conventional mold steel. The saving in manufacturing time of an injection mold made by RTT is about 30.3% compared with that of the fabricated conventional machining method. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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23 pages, 7793 KiB  
Article
Application of the NSGA-II Algorithm and Kriging Model to Optimise the Process Parameters for the Improvement of the Quality of Fresnel Lenses
by Hanjui Chang, Yue Sun, Rui Wang and Shuzhou Lu
Polymers 2023, 15(16), 3403; https://0-doi-org.brum.beds.ac.uk/10.3390/polym15163403 - 14 Aug 2023
Viewed by 789
Abstract
The Fresnel lens is an optical system consisting of a series of concentric diamond grooves. One surface of the lens is smooth, while the other is engraved with concentric circles of increasing size. Optical interference, diffraction, and sensitivity to the angle of incidence [...] Read more.
The Fresnel lens is an optical system consisting of a series of concentric diamond grooves. One surface of the lens is smooth, while the other is engraved with concentric circles of increasing size. Optical interference, diffraction, and sensitivity to the angle of incidence are used to design the microstructure on the lens surface. The imaging of the optical surface depends on its curvature. By reducing the thickness of the lens, light can still be focused at the same focal point as with a thicker lens. Previously, lenses, including Fresnel lenses, were made of glass due to material limitations. However, the traditional grinding and polishing methods for making Fresnel lenses were not only time-consuming, but also labour-intensive. As a result, costs were high. Later, a thermal pressing process using metal moulds was invented. However, the high surface tension of glass caused some detailed parts to be deformed during the pressing process, resulting in unsatisfactory Fresnel lens performance. In addition, the complex manufacturing process and unstable processing accuracy hindered mass production. This resulted in high prices and limited applications for Fresnel lenses. These factors prevented the widespread use of early Fresnel lenses. In contrast, polymer materials offer advantages, such as low density, light weight, high strength-to-weight ratios, and corrosion resistance. They are also cost effective and available in a wide range of grades. Polymer materials have gradually replaced optical glass and other materials in the manufacture of micro-optical lenses and other miniaturised devices. Therefore, this study focuses on investigating the manufacturing parameters of Fresnel lenses in the injection moulding process. We compare the quality of products obtained by two-stage injection moulding, injection compression moulding, and IMD (in-mould decoration) techniques. The results show that the optimal method is IMD, which reduces the nodal displacement on the Fresnel lens surface and improves the transmission performance. To achieve this, we first establish a Kriging model to correlate the process parameters with optimisation objectives, mapping the design parameters and optimisation objectives. Based on the Kriging model, we integrate the NSGA-II algorithm with the predictive model to obtain the Pareto optimal solutions. By analysing the Pareto frontier, we identify the best process parameters. Finally, it is determined that the average nodal displacement on the Fresnel surface is 0.393 mm, at a holding pressure of 320.35 MPa and a melt temperature of 251.40 °C. Combined with IMD technology, product testing shows a transmittance of 95.43% and an optimisation rate of 59.64%. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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21 pages, 5857 KiB  
Article
Enhancing High-Pressure Capillary Rheometer Viscosity Data Calculation with the Propagation of Uncertainties for Subsequent Cross-Williams, Landel, and Ferry (WLF) Parameter Fitting
by Martin Hubmann, Stephan Schuschnigg, Ivica Ðuretek, Jonas Groten and Clemens Holzer
Polymers 2023, 15(14), 3147; https://0-doi-org.brum.beds.ac.uk/10.3390/polym15143147 - 24 Jul 2023
Viewed by 1219
Abstract
Measuring the shear viscosity of polymeric melts is an extensive effort frequently performed in high-pressure capillary rheometers, where the pressures required to push the melt through a capillary at various temperatures and volumetric flow rates are recorded. Then, the viscosity values are obtained [...] Read more.
Measuring the shear viscosity of polymeric melts is an extensive effort frequently performed in high-pressure capillary rheometers, where the pressures required to push the melt through a capillary at various temperatures and volumetric flow rates are recorded. Then, the viscosity values are obtained through Bagley and Weissenberg–Rabinowitsch corrections involving parameter fitting. However, uncertainties in those conversions due to pressure variations and measurement inaccuracies (random errors) affect the accuracy of the consequently calculated viscosities. This paper proposes quantifying them through a propagation of uncertainties calculation. This has been experimentally demonstrated for a polycarbonate melt. In addition, the derived viscosity uncertainties were used for the weighted residual sum of squares parameter estimation of the Cross-WLF viscosity model and compared with the coefficients obtained using the standard residual sum of squares minimization approach. The motivation was that, by comparison, individual poorly measured viscosity values should have a less negative impact on the overall fit quality of the former. For validation, the rheometer measurements were numerically simulated with both fits. The simulations based on the Cross-WLF fit, including the derived viscosity uncertainties, matched the measured pressures ~16% more closely for shear rates below 1500 1/s. Considering the uncertainties led to more precise coefficients. However, both fits showed substantial deviations at higher shear rates, probably due to substantial non-isothermal flow conditions that prevailed during these measurements. A capillary rheometer experiment was also simulated using arbitrarily chosen Cross-WLF parameters to exclude such systematic errors. A normally distributed error was then applied to the simulated pressures before re-fitting the parameters. Again, taking advantage of the derived viscosity uncertainties, the fit could recover the initial parameters better. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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24 pages, 4671 KiB  
Article
Thermal Conductivity and Mechanical Properties of Polymer Composites with Hexagonal Boron Nitride—A Comparison of Three Processing Methods: Injection Moulding, Powder Bed Fusion and Casting
by Nu Bich Duyen Do, Kristin Imenes, Knut E. Aasmundtveit, Hoang-Vu Nguyen and Erik Andreassen
Polymers 2023, 15(6), 1552; https://0-doi-org.brum.beds.ac.uk/10.3390/polym15061552 - 21 Mar 2023
Cited by 4 | Viewed by 2663
Abstract
Materials providing heat dissipation and electrical insulation are required for many electronic and medical devices. Polymer composites with hexagonal boron nitride (hBN) may fulfil such requirements. The focus of this study is to compare composites with hBN fabricated by injection moulding (IM), powder [...] Read more.
Materials providing heat dissipation and electrical insulation are required for many electronic and medical devices. Polymer composites with hexagonal boron nitride (hBN) may fulfil such requirements. The focus of this study is to compare composites with hBN fabricated by injection moulding (IM), powder bed fusion (PBF) and casting. The specimens were characterised by measuring thermal conductivity, tensile properties, hardness and hBN particle orientation. A thermoplastic polyurethane (TPU) was selected as the matrix for IM and PBF, and an epoxy was the matrix for casting. The maximum filler weight fractions were 65%, 55% and 40% for IM, casting and PBF, respectively. The highest thermal conductivity (2.1 W/m∙K) was measured for an IM specimen with 65 wt% hBN. However, cast specimens had the highest thermal conductivity for a given hBN fraction. The orientation of hBN platelets in the specimens was characterised by X-ray diffraction and compared with numerical simulations. The measured thermal conductivities were discussed by comparing them with four models from the literature (the effective medium approximation model, the Ordóñez-Miranda model, the Sun model, and the Lewis-Nielsen model). These models predicted quite different thermal conductivities vs. filler fraction. Adding hBN increased the hardness and tensile modulus, and the tensile strength at high hBN fractions. The strength had a minimum as the function of filler fraction, while the strain at break decreased. These trends can be explained by two mechanisms which occur when adding hBN: reinforcement and embrittlement. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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15 pages, 5839 KiB  
Article
A Novel Design Method of an Evolutionary Mold Cooling Channel Using Biomimetic Engineering
by Jae Hyuk Choi, Jinsu Gim and Byungohk Rhee
Polymers 2023, 15(4), 798; https://0-doi-org.brum.beds.ac.uk/10.3390/polym15040798 - 04 Feb 2023
Cited by 6 | Viewed by 1922
Abstract
In this study, an evolutionary cooling channel, a new methodology for designing a conformal cooling channel, was proposed. This methodology was devised by imitating the way that a plant’s roots grow towards a nutrient-rich location. Additionally, Murray’s law was applied to increase the [...] Read more.
In this study, an evolutionary cooling channel, a new methodology for designing a conformal cooling channel, was proposed. This methodology was devised by imitating the way that a plant’s roots grow towards a nutrient-rich location. Additionally, Murray’s law was applied to increase the cooling efficiency through minimizing the pressure loss of the cooling water inside the cooling channel. The proposed method was applied to the specimen shape to verify the concept, and it was confirmed that efficient cooling was achieved by applying it to the headlamp lens cover part of an actual vehicle. When this methodology was applied, the temperature deviation of the part could be improved by about 46% in just third generations, and the pressure loss could be reduced by about 10 times or more compared to the result of applying the straight-line cooling channel. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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17 pages, 5743 KiB  
Article
Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding
by Joohyeong Jeon, Byungohk Rhee and Jinsu Gim
Polymers 2022, 14(24), 5548; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14245548 - 18 Dec 2022
Cited by 4 | Viewed by 2493
Abstract
Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply [...] Read more.
Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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15 pages, 4980 KiB  
Article
Effect of Increased Powder–Binder Adhesion by Backbone Grafting on the Properties of Feedstocks for Ceramic Injection Molding
by Laleh Ghasemi-Mobarakeh, Santiago Cano, Vahid Momeni, Dongyan Liu, Ivica Duretek, Gisbert Riess, Christian Kukla and Clemens Holzer
Polymers 2022, 14(17), 3653; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14173653 - 02 Sep 2022
Cited by 3 | Viewed by 1730
Abstract
The good interaction between the ceramic powder and the binder system is vital for ceramic injection molding and prevents the phase separation during processing. Due to the non-polar structure of polyolefins such as high-density polyethylene (HDPE) and the polar surface of ceramics such [...] Read more.
The good interaction between the ceramic powder and the binder system is vital for ceramic injection molding and prevents the phase separation during processing. Due to the non-polar structure of polyolefins such as high-density polyethylene (HDPE) and the polar surface of ceramics such as zirconia, there is not appropriate adhesion between them. In this study, the effect of adding high-density polyethylene grafted with acrylic acid (AAHDPE), with high polarity and strong adhesion to the powder, on the rheological, thermal and chemical properties of polymer composites highly filled with zirconia and feedstocks was evaluated. To gain a deeper understanding of the effect of each component, formulations containing different amounts of HDPE and or AAHDPE, zirconia and paraffin wax (PW) were prepared. Attenuated total reflection spectroscopy (ATR), scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and rotational and capillary rheology were used for the characterization of the different formulations. The ATR analysis revealed the formation of hydrogen bonds between the hydroxyl groups on the zirconia surface and AAHDPE. The improved powder-binder adhesion in the formulations with more AAHDPE resulted in a better powder dispersion and homogeneous mixtures, as observed by SEM. DSC results revealed that the addition of AAHDPE, PW and zirconia effect the melting and crystallization temperature and crystallinity of the binder, the polymer-filled system and feedstocks. The better powder--binder adhesion and powder dispersion effectively decreased the viscosity of the highly filled polymer composites and feedstocks with AAHDPE; this showed the potential of grafted polymers as binders for ceramic injection molding. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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24 pages, 7993 KiB  
Article
Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts
by Saeid Saeidi Aminabadi, Paul Tabatabai, Alexander Steiner, Dieter Paul Gruber, Walter Friesenbichler, Christoph Habersohn and Gerald Berger-Weber
Polymers 2022, 14(17), 3551; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14173551 - 29 Aug 2022
Cited by 7 | Viewed by 4889
Abstract
Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. [...] Read more.
Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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18 pages, 6808 KiB  
Article
Mechanical Properties of Injection Molded PP/PET-Nanofibril Composites and Foams
by Lun Howe Mark, Chongxiang Zhao, Raymond K. M. Chu and Chul B. Park
Polymers 2022, 14(14), 2958; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14142958 - 21 Jul 2022
Cited by 5 | Viewed by 2195
Abstract
The creation and application of PET nanofibrils for PP composite reinforcement were studied. PET nanofibrils were fibrillated within a PP matrix using a spunbond process and then injection molded to test for the end-use properties. The nanofibril reinforcement helped to provide higher tensile [...] Read more.
The creation and application of PET nanofibrils for PP composite reinforcement were studied. PET nanofibrils were fibrillated within a PP matrix using a spunbond process and then injection molded to test for the end-use properties. The nanofibril reinforcement helped to provide higher tensile and flexural performance in solid (unfoamed) injection molded parts. With foam injection molding, the nanofibrils also helped to improve and refine the microcellular morphology, which led to improved performance. Easily and effectively increasing the strength of a polymeric composite is a goal for many research endeavors. By creating nanoscale fibrils within the matrix itself, effective bonding and dispersion have already been achieved, overcoming the common pitfalls of fiber reinforcement. As blends of PP and PET are drawn in a spunbond system, the PET domains are stretched into nanoscale fibrils. By adapting the spunbonded blends for use in injection molding, both solid and foamed nanocomposites are created. The injection molded nanocomposites achieved increased in both tensile and flexural strength. The solid and foamed tensile strength increased by 50 and 100%, respectively. In addition, both the solid and foamed flexural strength increased by 100%. These increases in strength are attributed to effective PET nanofibril reinforcement. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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16 pages, 3809 KiB  
Article
Impact of Melt Processing Conditions on the Degradation of Polylactic Acid
by Thamer Aldhafeeri, Mansour Alotaibi and Carol Forance Barry
Polymers 2022, 14(14), 2790; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14142790 - 08 Jul 2022
Cited by 10 | Viewed by 3019
Abstract
To reduce the degradation of polylactic acid (PLA) during processing, which reduces the molecular weight of PLA and its properties, prior studies have recommended low processing temperatures. In contrast, this work investigated the impact of four factors affecting shear heating (extruder type, screw [...] Read more.
To reduce the degradation of polylactic acid (PLA) during processing, which reduces the molecular weight of PLA and its properties, prior studies have recommended low processing temperatures. In contrast, this work investigated the impact of four factors affecting shear heating (extruder type, screw configuration, screw speed, and feed rate) on the degradation of PLA. The polylactic acid was processed using a quad screw extruder (QSE) and a comparable twin screw extruder (TSE), two screw configurations, higher screw speeds, and several feed rates. The processed PLA was characterized by its rheological, thermal, and material composition properties. In both screw configurations, the QSE (which has a greater free volume) produced 3–4 °C increases in melt temperature when the screw speed was increased from 400 rpm to 1000 rpm, whereas the temperature rise was 24–25 °C in the TSE. PLA processed at low screw speeds, however, exhibited greater reductions in molecular weight—i.e., 9% in the QSE and 7% in the TSE. Screw configurations with fewer kneading blocks, and higher feed rates in the QSE, reduced degradation of PLA. At lower processing temperatures, it was found that an increase in melt temperature and shear rate did not significantly contribute to the degradation of PLA. Reducing the residence time during processing minimized the degradation of PLA in a molten state. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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23 pages, 1746 KiB  
Article
Occupational Safety Analysis for COVID-Instigated Repurposed Manufacturing Lines: Use of Nanomaterials in Injection Moulding
by Spyridon Damilos, Stratos Saliakas, Ioannis Kokkinopoulos, Panagiotis Karayannis, Melpo Karamitrou, Aikaterini-Flora Trompeta, Costas Charitidis and Elias P. Koumoulos
Polymers 2022, 14(12), 2418; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14122418 - 14 Jun 2022
Cited by 1 | Viewed by 2693
Abstract
The COVID-19 pandemic instigated massive production of critical medical supplies and personal protective equipment. Injection moulding (IM) is considered the most prominent thermoplastic part manufacturing technique, offering the use of a large variety of feedstocks and rapid production capacity. Within the context of [...] Read more.
The COVID-19 pandemic instigated massive production of critical medical supplies and personal protective equipment. Injection moulding (IM) is considered the most prominent thermoplastic part manufacturing technique, offering the use of a large variety of feedstocks and rapid production capacity. Within the context of the European Commission-funded imPURE project, the benefits of IM have been exploited in repurposed IM lines to accommodate the use of nanocomposites and introduce the unique properties of nanomaterials. However, these amendments in the manufacturing lines highlighted the need for targeted and thorough occupational risk analysis due to the potential exposure of workers to airborne nanomaterials and fumes, as well as the introduction of additional occupational hazards. In this work, a safety-oriented failure mode and effects analysis (FMEA) was implemented to evaluate the main hazards in repurposed IM lines using acrylonitrile butadiene styrene (ABS) matrix and silver nanoparticles (AgNPs) as additives. Twenty-eight failure modes were identified, with the upper quartile including the seven failure modes presenting the highest risk priority numbers (RPN), signifying a need for immediate control action. Additionally, a nanosafety control-banding tool allowed hazard classification and the identification of control actions required for mitigation of occupation risks due to the released airborne silver nanoparticles. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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27 pages, 3611 KiB  
Article
Natural Rubber Blend Optimization via Data-Driven Modeling: The Implementation for Reverse Engineering
by Allen Jonathan Román, Shiyi Qin, Julio C. Rodríguez, Leonardo D. González, Victor M. Zavala and Tim A. Osswald
Polymers 2022, 14(11), 2262; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14112262 - 31 May 2022
Cited by 9 | Viewed by 2349
Abstract
Natural rubber formulation methodologies implemented within industry primarily implicate a high dependence on the formulator’s experience as it involves an educated guess-and-check process. The formulator must leverage their experience to ensure that the number of iterations to the final blend composition is minimized. [...] Read more.
Natural rubber formulation methodologies implemented within industry primarily implicate a high dependence on the formulator’s experience as it involves an educated guess-and-check process. The formulator must leverage their experience to ensure that the number of iterations to the final blend composition is minimized. The study presented in this paper includes the implementation of blend formulation methodology that targets material properties relevant to the application in which the product will be used by incorporating predictive models, including linear regression, response surface method (RSM), artificial neural networks (ANNs), and Gaussian process regression (GPR). Training of such models requires data, which is equal to financial resources in industry. To ensure minimum experimental effort, the dataset is kept small, and the model complexity is kept simple, and as a proof of concept, the predictive models are used to reverse engineer a current material used in the footwear industry based on target viscoelastic properties (relaxation behavior, tanδ, and hardness), which all depend on the amount of crosslinker, plasticizer, and the quantity of voids used to create the lightweight high-performance material. RSM, ANN, and GPR result in prediction accuracy of 90%, 97%, and 100%, respectively. It is evident that the testing accuracy increases with algorithm complexity; therefore, these methodologies provide a wide range of tools capable of predicting compound formulation based on specified target properties, and with a wide range of complexity. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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15 pages, 4841 KiB  
Article
A Water-Soluble Core for Manufacturing Hollow Injection-Molded Products
by Chung-Chih Lin and Chao-Long Yang
Polymers 2022, 14(11), 2185; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14112185 - 27 May 2022
Cited by 1 | Viewed by 3241
Abstract
To manufacture a complicated hollow product without any assembly process, for example, the plastic intake manifold, is difficult by the traditional injection molding method. The fusible-core technique, which uses a low-melting-point alloy as a sacrificial core, was developed to solve this problem; however, [...] Read more.
To manufacture a complicated hollow product without any assembly process, for example, the plastic intake manifold, is difficult by the traditional injection molding method. The fusible-core technique, which uses a low-melting-point alloy as a sacrificial core, was developed to solve this problem; however, the limited selection of resin type and the huge capital investment have caused this technique to spread slowly. In this work, a novel method is established that can produce similar products without the limitation of resin type, as well as a lower-energy-consumption process. The concept of the enveloped core defined by a water-soluble core assembled with a shell is proposed herein; it provides both rigidity and toughness to resist the pressure during the injection molding process. The shape of the enveloped core equals the internal contour of the designated product. An insert molding process was introduced to cover the enveloped core with a skin layer. Cut out the end of the enveloped core and immerse it into a water bath. When the water-soluble core inside the shell is dissolved, the product with a special internal contour is accomplished. A tee-joint is presented to demonstrate how the proposed method can be utilized. The optimal ingredient of the core and processing parameters are determined by the Taguchi method. The result shows that the proposed product is molded successfully when the compressive strength of the core is larger than 2 MPa. In addition, the eccentricity measurement of internal contour of the optimal sample exhibits a 56% improvement, and the required time for the core removal is less than 154 s. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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14 pages, 7562 KiB  
Article
Using Gas Counter Pressure and Combined Technologies for Microcellular Injection Molding of Thermoplastic Polyurethane to Achieve High Foaming Qualities and Weight Reduction
by Shia-Chung Chen, Kuan-Hua Lee, Che-Wei Chang, Tzu-Jeng Hsu and Ching-Te Feng
Polymers 2022, 14(10), 2017; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14102017 - 15 May 2022
Cited by 7 | Viewed by 2218
Abstract
Microcellular injection molding technology (MuCell®) using supercritical fluid (SCF) as a foaming agent offers many advantages, such as material and energy savings, low cycle time, cost-effectiveness, and the dimensional stability of products. MuCell® has attracted great attention for applications in [...] Read more.
Microcellular injection molding technology (MuCell®) using supercritical fluid (SCF) as a foaming agent offers many advantages, such as material and energy savings, low cycle time, cost-effectiveness, and the dimensional stability of products. MuCell® has attracted great attention for applications in the automotive, packaging, sporting goods, and electrical parts industries. In view of the environmental issues, the shoe industry, particularly for midsole parts, is also seriously considering using physical foaming to replace the chemical foaming process. MuCell® is thus becoming one potential processing candidate. Thermoplastic polyurethane (TPU) is a common material for molding the outsole of shoes because of its outstanding properties such as hardness, abrasion resistance, and elasticity. Although many shoe manufacturers have tried applying Mucell® processes to TPU midsoles, the main problem remaining to be overcome is the non-uniformity of the foaming cell size in the molded midsole. In this study, the MuCell® process combined with gas counter pressure (GCP) technology and dynamic mold temperature control (DMTC) were carried out for TPU molding. The influence of various molding parameters including SCF dosage, injection speed, mold temperature, gas counter pressure, and gas holding time on the foaming cell size and the associated size distribution under a target weight reduction of 60% were investigated in detail. Compared with the conventional MuCell® process, the implementation of GCP technology or DMTC led to significant improvement in foaming cell size reduction and size uniformity. Further improvement could be achieved by the simultaneous combination of GCP with DMT, and the resulting cell density was about fifty times higher. The successful possibility for the microcellular injection molding of TPU shoe midsoles is greatly enhanced. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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24 pages, 6925 KiB  
Article
Effects of Input Parameter Range on the Accuracy of Artificial Neural Network Prediction for the Injection Molding Process
by Junhan Lee, Dongcheol Yang, Kyunghwan Yoon and Jongsun Kim
Polymers 2022, 14(9), 1724; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14091724 - 23 Apr 2022
Cited by 9 | Viewed by 1643
Abstract
Artificial neural network (ANN) is a representative technique for identifying relationships that contain complex nonlinearities. However, few studies have analyzed the ANN’s ability to represent nonlinear or linear relationships between input and output parameters in injection molding. The melt temperature, mold temperature, injection [...] Read more.
Artificial neural network (ANN) is a representative technique for identifying relationships that contain complex nonlinearities. However, few studies have analyzed the ANN’s ability to represent nonlinear or linear relationships between input and output parameters in injection molding. The melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time were chosen as input parameters, and the mass, diameter, and height of the injection molded product as output parameters to construct an ANN model and its prediction performance was compared with those of linear regression and second-order polynomial regression. Following the preliminary experiment results, the learning data sets were divided into two groups, i.e., one showed linear relation between the mass of the final product and the range of packing time (linear relation group), and the other showed clear nonlinear relation (nonlinear relation group). The predicted results of ANN were relatively better than those of linear regression and second-order polynomial for both linear and nonlinear relation groups in our specific data sets of the present study. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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13 pages, 3930 KiB  
Article
Innovative Injection Molding Process for the Fabrication of Woven Fabric Reinforced Thermoplastic Composites
by Euichul Jeong, Yongdae Kim, Seokkwan Hong, Kyunghwan Yoon and Sunghee Lee
Polymers 2022, 14(8), 1577; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14081577 - 13 Apr 2022
Cited by 11 | Viewed by 4383
Abstract
Woven fabric reinforced thermoplastic composites have been gaining significant attention as a lightweight alternative to metal in various industrial fields owing to their high stiffness and strength. Conventional manufacturing processes of woven fabric reinforced thermoplastic composites can be divided into two steps: first, [...] Read more.
Woven fabric reinforced thermoplastic composites have been gaining significant attention as a lightweight alternative to metal in various industrial fields owing to their high stiffness and strength. Conventional manufacturing processes of woven fabric reinforced thermoplastic composites can be divided into two steps: first, the manufacturing of intermediate material, known as prepreg; then, the formation of the final products from the prepregs. This two-step process increases the manufacturing cost and time of the final composite products. This study demonstrated that woven fabric reinforced thermoplastic composites could be fabricated by an innovative injection molding process instead of the two-step process. A structure placing an extra mesh, which is a new and key component, on the mold-side of woven fabric was devised so that the thermoplastic matrix could be impregnated up to the surface of the woven fabric during injection molding. Tensile tests were performed in the direction parallel to the yarns of the fabric on the injection-molded composites to confirm their mechanical properties. As a result, it was possible to fabricate woven fabric reinforced thermoplastic composites with increased mechanical properties using injection molding without prepreg, and the composites could be molded with a much shorter cycle time than the conventional process, such as thermoforming or over-molding process. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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12 pages, 3815 KiB  
Article
Conventional and Microcellular Injection Molding of a Highly Filled Polycarbonate Composite with Glass Fibers and Carbon Black
by Galip Yilmaz, Apichart Devahastin and Lih-Sheng Turng
Polymers 2022, 14(6), 1193; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14061193 - 16 Mar 2022
Cited by 1 | Viewed by 2712
Abstract
Conventional solid injection molding (CIM) and microcellular injection molding (MIM) of a highly filled polycarbonate (PC) composite with glass fibers and carbon black were performed for molding ASTM tensile test bars and a box-shape part with variable wall thickness. A scanning electron microscope [...] Read more.
Conventional solid injection molding (CIM) and microcellular injection molding (MIM) of a highly filled polycarbonate (PC) composite with glass fibers and carbon black were performed for molding ASTM tensile test bars and a box-shape part with variable wall thickness. A scanning electron microscope (SEM) was used to examine the microstructure at the fractured surface of the tensile test bar samples. The fine and uniform cellular structure suggests that the PC composite is a suitable material for foaming applications. Standard tensile tests showed that, while the ultimate strength and elongation at break were lower for the foamed test bars at 4.0–11.4% weight reduction, their specific Young’s modulus was comparable to that of their solid counterparts. A melt flow and transition model was proposed to explain the unique, irregular “tiger-stripes” exhibited on the surface of solid test bars. Increasing the supercritical fluid (SCF) dosage and weight reduction of foamed samples resulted in swirl marks on the part surface, making the tiger-stripes less noticeable. Finally, it was found that an injection pressure reduction of 25.8% could be achieved with MIM for molding a complex box-shaped part in a consistent and reliable fashion. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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15 pages, 3443 KiB  
Article
Effects of Injection Molding Parameters on Properties of Insert-Injection Molded Polypropylene Single-Polymer Composites
by Jian Wang, Qianchao Mao, Nannan Jiang and Jinnan Chen
Polymers 2022, 14(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/polym14010023 - 22 Dec 2021
Cited by 16 | Viewed by 4221
Abstract
The reinforcement and matrix of a polymer material can be composited into a single polymer composite (SPC), which is light weight, high strength, and has easy recyclability. The insert injection molding process can be used to realize the multiple production of SPC products [...] Read more.
The reinforcement and matrix of a polymer material can be composited into a single polymer composite (SPC), which is light weight, high strength, and has easy recyclability. The insert injection molding process can be used to realize the multiple production of SPC products with a short cycle time and wide processing temperature window. However, injection molding is a very complicated process; the influence of several important parameters should be determined to help in the future tailoring of SPCs to specific applications. The effects of varying barrel temperature, injection pressure, injection speed, and holding time on the properties of the insert-injection molded polypropylene (PP) SPC parts were investigated. It was found that the sample weight and tensile properties of the PP SPCs varied in different rules with the variations of these four parameters. The barrel temperature has a significant effect, followed by the holding time and injection pressure. Suitable parameter values should be determined for enhanced mechanical properties. Based on the tensile strength, a barrel temperature of 260 °C, an injection pressure of 127.6 MPa, an injection speed of 0.18 m/s, and a holding time of 60 s were determined as the optimum processing conditions. The best tensile strength and peel strength were up to 120 MPa and 19.44 N/cm, respectively. Full article
(This article belongs to the Special Issue Advances in Polymers Processing and Injection Molding)
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