Design, Manufacturing and Injection Molding of Polymeric Materials and Composites

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 November 2023) | Viewed by 4818

Special Issue Editors

1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
2. Taizhou Institute, Zhejiang University of Technology, Taizhou 318014, China
Interests: manufacturing of polymer products; Intelligent manufacturing in polymer molding; advanced mold technology innovations such as rapid change of mold temperature and gas assist
Special Issues, Collections and Topics in MDPI journals
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: mechanical engineering; injection molded products; conceptual design; mechatronics; design structure matrix
Special Issues, Collections and Topics in MDPI journals
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: optimization design of polymer product; digital design; structural optimization; design based on artificial intelligence

Special Issue Information

Dear Colleagues,

Polymer is one of most common materials for product manufacturing. Product quality is influenced by the manufacturing process, and also greatly influenced by the design. This Special Issue will focus on design and manufacturing methodologies for polymer products.

It is with great pleasure that we invite you to submit a manuscript related to design, manufacture, and injection molding for this special issue. Remarkable contributions including research articles, communications, and reviews from experts all over the world are welcome.

Prof. Dr. Jiquan Li
Prof. Dr. Shaofei Jiang
Dr. Xiang Peng
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

  • artificial intelligence
  • knowledge processing
  • digital design
  • intelligent manufacturing
  • mold design
  • injection molding
  • manufacture of polymer products
  • optimization design of polymer products

Published Papers (5 papers)

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Research

15 pages, 6334 KiB  
Article
Effects of Different Die Metals on the Performance and Friction and Wear of Composite Materials during the Extrusion Process
by Hong Liu and Chuansheng Wang
Polymers 2023, 15(24), 4684; https://doi.org/10.3390/polym15244684 - 12 Dec 2023
Viewed by 740
Abstract
Extrusion technology is widely utilized in the rubber processing industry, with the extruder serving as the core equipment. As mixed rubber enters the extruder, it undergoes conveyance and plasticization, ultimately forming specific shapes and dimensions upon extrusion. The extruder head is a crucial [...] Read more.
Extrusion technology is widely utilized in the rubber processing industry, with the extruder serving as the core equipment. As mixed rubber enters the extruder, it undergoes conveyance and plasticization, ultimately forming specific shapes and dimensions upon extrusion. The extruder head is a crucial component, playing a key role in achieving the final product’s required size and shape. Factors such as its structure, materials, and manufacturing processes significantly impact the efficiency, product quality, and sustainability of the extrusion process. However, prolonged operation leads to severe wear of the extruder head, adversely affecting rubber product quality. Additionally, extruder head processing poses challenges, with maintenance and repair being complex procedures. Therefore, exploring a wear-resistant, long-lasting metal material for the extruder head without compromising mixed rubber performance is essential. This study focuses on severely worn extruder head metal materials, comparing wear levels after friction with STELLITE 6 alloy, Hastelloy C-276 alloy, 38CrMoAlA, and tungsten carbide with composite rubber. Results show that compared to the NR/BR composite material after Hastelloy C-276 alloy friction, rubber Payne effect increased by 4.4% (38CrMoAl), 3.2% (STELLITE 6), and 4.6% (tungsten carbide). Similarly, rubber dispersion decreased by 9.4% (38CrMoAl), 4.7% (STELLITE 6), and 9.8% (tungsten carbide). Rolling resistance increased by 18.1% (38CrMoAl), 16% (STELLITE 6), and 23.4% (tungsten carbide). Friction coefficient increased by 3.5% (38CrMoAl), 2.8% (STELLITE 6), and 4.3% (tungsten carbide). Wear volume increased by 39.3% (38CrMoAl), 45.3% (STELLITE 6), and 48.9% (tungsten carbide). Specifically, using Hastelloy C-276 alloy as the extruder head metal material yields the best NR/BR composite material dispersion, highest ten times tear strength, excellent anti-wet skid resistance, and minimum rolling resistance. Conversely, using the other alloys results in varying reductions in the physical and mechanical properties of NR/BR composite materials. This research is crucial for improving rubber product quality and extending extruder head lifespan. Full article
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26 pages, 5769 KiB  
Article
A Study on the Architecture of Artificial Neural Network Considering Injection-Molding Process Steps
by Junhan Lee, Jongsun Kim and Jongsu Kim
Polymers 2023, 15(23), 4578; https://doi.org/10.3390/polym15234578 - 30 Nov 2023
Cited by 1 | Viewed by 655
Abstract
In this study, an artificial neural network (ANN) was established to predict product properties (mass, diameter, height) using six process conditions of the injection-molding process (melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time) as input parameters. The injection-molding [...] Read more.
In this study, an artificial neural network (ANN) was established to predict product properties (mass, diameter, height) using six process conditions of the injection-molding process (melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time) as input parameters. The injection-molding process consists of continuous sequential stages, including the injection stage, packing stage, and cooling stage. However, the related research tends to have an insufficient incorporation of structural characteristics based on these basic process stages. Therefore, in order to incorporate these process stages and characteristics into the ANN, a process-based multi-task learning technique was applied to the connection between the input parameters and the front-end of the hidden layer. This resulted in the construction of two network structures, and their performance was evaluated by comparing them with the typical network structure. The results showed that a multi-task learning architecture that incorporated process-level specific structures in the connections between the input parameters and the front end of the hidden layer yielded relatively better root mean square errors (RMSEs) values than a conventional neural network architecture, by as much as two orders of magnitude. Based on these results, this study has provided guidance for the construction of artificial neural networks for injection-molding processes that incorporates process-stage specific features and structures in the architecture. Full article
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15 pages, 3692 KiB  
Article
Determination of Correlations between Melt Quality and the Screw Performance Index in the Extrusion Process
by Dorte Trienens, Volker Schöppner and Robin Bunse
Polymers 2023, 15(16), 3427; https://doi.org/10.3390/polym15163427 - 16 Aug 2023
Viewed by 761
Abstract
For polymer-processing extruders, designing screws via analytical computational models is helpful for reducing experimental costs. However, the current simulation programs cannot predict the melt quality at the screw tip with sufficient accuracy. There are a number of definitions of melt quality in the [...] Read more.
For polymer-processing extruders, designing screws via analytical computational models is helpful for reducing experimental costs. However, the current simulation programs cannot predict the melt quality at the screw tip with sufficient accuracy. There are a number of definitions of melt quality in the literature. This paper will review some of these definitions and present how melt quality can be assessed in subsequent work. In this paper, both the thermal and material homogeneity of the melt quality are examined for correlations with the screw performance index. If correlations exist with the screw performance index determined from direct experimental measurement data, these can be used as target values for developing a melt quality prediction model. Full article
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23 pages, 11740 KiB  
Article
Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process
by Mattia Perin, Youngbin Lim, Guido A. Berti, Taeyong Lee, Kai Jin and Luca Quagliato
Polymers 2023, 15(14), 3094; https://doi.org/10.3390/polym15143094 - 19 Jul 2023
Cited by 1 | Viewed by 1161
Abstract
Fiber reinforcement orientation in thermoplastic injection-molded components is both a strength as well as a weak point of this largely employed manufacturing process. Optimizing the fiber orientation distribution (FOD) considering the shape of the part and the applied loading conditions allows for enhancing [...] Read more.
Fiber reinforcement orientation in thermoplastic injection-molded components is both a strength as well as a weak point of this largely employed manufacturing process. Optimizing the fiber orientation distribution (FOD) considering the shape of the part and the applied loading conditions allows for enhancing the mechanical performances of the produced parts. Henceforth, this research proposes an algorithm to identify the best injection gate (IG) location/s starting from a 3D model and a user-defined load case. The procedure is composed of a first Visual Basic Architecture (VBA) code that automatically sets and runs Finite Volume Method (FVM) simulations to find the correlation between the fiber orientation tensor (FOT) and the IG locations considering single and multiple gates combinations up to three points. A second VBA code elaborates the results and builds a dataset considering the user-defined loading and constraint conditions, allowing the assignment of a score to each IG solution. Three geometrical components of increasing complexity were considered for a total of 1080 FVM simulations and a total computational time of ~390 h. The search for the best IG location has been further expanded by training a Machine Learning (ML) model based on the Gradient Boosting (GB) algorithm. The training database (DB) is based on FVM simulations and was expanded until a satisfactory prediction accuracy higher than 90% was achieved. The enhancement of the local FOD on the critical regions of three components was verified and showed an average improvement of 26.9% in the stiffness granted by a high directionality of the fibers along the load path. Finite element method (FEM) simulations and laboratory experiments on an industrial pump housing, injection-molded with a polyamide-66 reinforced with 30% of short glass fibers (PA66-30GF) material were also carried out to validate the FVM-FEM simulation frame and showed a 16.4% local stiffness improvement in comparison to the currently employed IG solution. Full article
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12 pages, 3891 KiB  
Article
Effects of Cavity Thickness on the Replication of Micro Injection Molded Parts with Microstructure Array
by Shaofei Jiang, Yuansong Zhang, Haowei Ma, Xiaoqiang Zha, Xiang Peng, Jiquan Li and Chunfu Lu
Polymers 2022, 14(24), 5471; https://doi.org/10.3390/polym14245471 - 14 Dec 2022
Cited by 1 | Viewed by 1058
Abstract
Parts with microstructure arrays have been widely used in biotechnologies and optical technologies, and their performances are affected by replication uniformity. The uniformity of the microstructure is still a challenge in micro-injection molded parts and is greatly affected by the cavity thickness and [...] Read more.
Parts with microstructure arrays have been widely used in biotechnologies and optical technologies, and their performances are affected by replication uniformity. The uniformity of the microstructure is still a challenge in micro-injection molded parts and is greatly affected by the cavity thickness and process parameters. In this study, the replication uniformity of microstructures is experimentally investigated. The relationship between the replication uniformity and cavity thickness was explored through single-factor experiments. Additionally, the impacts of the process parameters on the replication uniformity were also studied through uniform design experiments. A regression equation was established to describe the quantitative relationship between the important parameters and replication uniformity. The results showed that the replication uniformity of microstructures increases by 39.82% between the cavity with the thickness of 0.5 mm and a cavity of 0.7 mm. In addition, holding time is the most significant factor influencing the replication uniformity, followed by mold temperature, melt temperature, and injection speed. It is concluded that the thickness of cavity and the process parameters have significant influence on the replication uniformity. The experimental results provide important data on how to improve the replication uniformity of parts with microstructure arrays. Full article
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