SPE Library

The SPE Library contains thousands of papers, presentations, journal briefs and recorded webinars from the best minds in the Plastics Industry. Spanning almost two decades, this collection of published research and development work in polymer science and plastics technology is a wealth of knowledge and information for anyone involved in plastics.

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Conference Proceedings

PUDIS - Plastic packing Unique Device Identification System

Johannes Ullrich, March 2023

Injection molding is one of the essential production processes in the processing of plastics into components. The thermoplastics used in this process are divided into amorphous and semi-crystalline solidification on the basis of their properties. In the case of semi-crystalline plastics, crystallization nuclei form below the crystallization temperature, from which spherulitic structures grow. The temperature regime in the injection molding process influences the degree of crystallization and the microstructure in such a way that, depending on the process conditions, high or low degrees of crystallinity or fine or coarse microstructures are formed, which are also additionally influenced by shear. The degree of crystallization in turn influences the properties of the semi-crystalline plastic. Depending on the application, a certain morphology can thus be advantageous in relation to a complete component, but also only in individual areas. The aim of this study is therefore to provide scientific evidence of the manufacturability of a selectively adjustable degree of crystallinity within a functional component made from a semi-crystalline plastic in an injection molding process. The relationship between temperature control in the process, heating and cooling rates and crystallinity as well as morphology should be investigated using the semi-crystalline materials POM, PBT, PPS, PP, PET, PA66 and PA6. The starting point was the development of an article geometry and mold concept adapted to the intended manufacturing process (injection molding with variothermal tempering) and the intended analysis methods (DSC, DMA, short-term tensile test, polarization microscopy). According to current findings, it can be stated that at least the morphology/spherulite size and the degree of crystallinity can be influenced in the injection molding process if suitable process conditions are selected, whereby the material properties can be changed accordingly.

Hybrid Modeling of the Injection Molding Process Using PINNs

M. Wenzel, S. R. Raisch, S. Saad, M. Schmitz, C. Hopmann, March 2023

Machine Learning (ML) methods offer a great opportunity to model the complex behavior of the injection molding process. They have the potential to predict the impact of various process and material parameters on the resulting part quality. The dynamic behavior of the injection molding process and the associated effort to collect process data are still a major challenge for the application of ML methods. In this work, a hybrid approach is proposed to reduce the amount of data required to describe the injection molding process by combining process data with further process knowledge such as material models, flow equations and high-fidelity numerical simulations. A Physics-Informed Neural Network (PINN) is used to model the relationships between process settings and physical process parameters. With the help of PINN, the governing differential equations and material models of the injection molding process are integrated into a machine learning algorithm. High-fidelity injection molding simulation results are used to further train and validate the physics-based process model. This approach leads to a data efficient surrogate model of a high-fidelity injection molding simulation.

Digital Twin of Injection Molding: Controlling Quality Properties of Recycled Plastics by Using Self Re-Training Machine Learning Algorithms

Marco Klute, March 2023

Interconnectivity options for injection molding machines, e.g., communication interfaces such as OPC-UA, allow machine and process variables to be recorded in high resolution. This data can be used to improve quality monitoring, which may contribute to cost reductions by minimizing production waste or increasing the use of recycled material. Currently, for example, only small amounts of production waste can be recycled back into the process because the component quality otherwise shows a high fluctuation due to changes in material properties. Automated quality control and adjustment of the process parameters can counteract these fluctuations and thus enable a higher proportion of recyclate to be used in production. In addition to the resulting savings, production costs can also be reduced by increasing product quality. This reduces the rate of production waste, for example, which contributes significantly to more economical and sustainable production. For these reasons, control of the quality properties of the manufactured components has been sought in injection molding for decades. However, the control of component properties requires their direct measurement within the production cycle, which is often not possible, very cost-intensive and/or cannot be carried out non-destructively. For this reason, it is common practice to control machine or process variables that correlate with component quality instead. However, the injection molding process is affected by numerous non-measurable disturbance variables which influence the transmission behavior of the machine, so that identical process parameters do not result in identical process variable curves and finally do not result in identical component quality. Thus, it is necessary to develop an assistance system based on a digital twin of the injection molding process, which supports the machine operator in setting the process parameters of the injection molding machine in such a way that a desired part quality results. As part of this study, a digital twin of a real injection molding process was developed on an Arburg injection molding machine (Allrounder 470S, ARBURG GmbH + Co KG, Lossburg, Germany). Essentially, the work involved the following steps: Setting up a quality measuring cell that records the relevant component qualities, developing a software module that records all relevant machine and process variables cycle-related as single values and trajectories, and modeling the digital twin that predicts the resulting component quality on the basis of the recorded variables. A laboratory scale and a digital measuring projector were used to determine the quality characteristics, so that the component weight and dimensional accuracies, e.g., diameter and width, were measured from the injection-molded tamper-evident closure after each cycle and assigned to the recorded machine and process variables of the corresponding cycle. The machine and process variables were retrieved via the OPC-UA interface of the injection molding machine. Process variable trajectories, such as cavity pressure, cavity temperature, injection pressure and injection speed curves, must be recorded in high resolution for reliable modeling due to the short duration of the injection process. All machine and process variables as well as the quality variables measured after the cycle are stored in a database file assigned to the cycle number. With the data retrieved from a design of experiment divided into training and test data, different static and dynamic model structures were tested according to their best fit rates (BFR). The different modelling approaches can be divided into three categories: 1) Setpoint model: The machine setpoints are mapped directly to the resulting part quality. A Polynomial Regression (PR) model and a Multilayer Perceptron (MLP) were employed. 2) Measurement-features model: The final part quality is predicted from the machine setpoints and from features extracted from process measurements based on expert knowledge, i.e., maximum cavity pressure and temperature, or temperature in the cavity at the beginning of the injection phase. As for the setpoint models a PR model and a MLP were employed. 3) Internal dynamics model: A modern type of Recurrent Neural Networks (RNN), a Gated Recurrent Unit (GRU) is used to predict batch-end product quality from process value trajectories. The internal state of the GRU is mapped to the output via a feedforward Neural Network with a nonlinear hidden layer and a linear output layer. Since the injection molding process is a time-varying process switching between different machine internal controllers, the model was also divided into the three major phases of the processing cycle (injection, packing, cooling). Since the third phase maps the internal state to the output, it is additionally equipped with an MLP. If the BFR of the individual models are compared, it can be seen that even the simple setpoint models can predict the component quality very well. The 10th degree PR model, for example, achieves a BFR of 90%. The fact that the models which predict the part quality only on the basis of the parameters set on the machine achieve very good results in this test series could be due, among other things, to the fact that all disturbance variables affecting the process were excluded or kept constant as far as possible during the test. For the models that take into account features calculated from the trajectories in addition to the setpoints, the MLP with ten neurons in the hidden layer achieved the highest BFR of 93%. Compared to these two static model approaches, the dynamic GRU achieves only marginally better BFR. On the one hand, it is astonishing that these models can predict the part quality so well based on the raw data without any prior knowledge from experts; on the other hand, the high computational effort for the formation of a digital twin, especially for short cycle times, cannot be justified. For the actual digital twin, static model approaches were therefore used whose computing times are significantly shorter. While the pre-trained twin receives the new machine and process data after each cycle in live operation of the injection molding machine and predicts the component quality from this, it then compares this prediction with the measured quality variables and re-trains itself based on the error. In this way, it learns to describe the process even better over time. Using backpropagation, the digital twin can also calculate the optimum machine settings for a desired target variable of the quality characteristics.

Foundations for Enabling a Smart Injection Molding Factory

Patrick Sapel, Christian Hopmann, March 2023

Accompanying the era of digitalization into business leads to a new manufacturing concept called "Smart Factory". Smart Factories promise more efficient production processes, manifesting in integrated autonomous asset configuration and data -based decisionsupport in the operator's daily business. Although the origin of Smart Factories lies in 2011, it can be identified that integrated Smart Factories are rarely implemented and often comprise one encapsulated, specific use case. One main reason is a lack of standard semantics that serves as a basis for asset communication and providing decision-support. This contribution presents the foundations for enabling an integrated smart factory within the injection molding domain. Considering the Reference Architecture Model Industry 4.0 (RAMI 4.0) as a basic architecture for Industry 4.0, this contribution gives insights into building Digital Twins and Digital Shadows for the injection molding domain. Furthermore, it demonstrates how Digital Twins and Digital Shadows can interact autonomously by introducing semantics and dictionaries. Subsequently, the modelling of a real use case in the field of production planning for injection molding demonstrates that RAMI 4.0 is eligible as tool for enabling smart injection molding factories, so faster and valid decision-support for production planners can be achieved.

A New Approach for the Local Determination of Energy Input in CO-Rotating Twin Screw Extruders Based on Screw Torsion

Laura Austermeier, March 2023

A targeted energy input in the melting zone of co-rotating twin screw extruders, which is sufficient to plasticize the material without damaging it, is one of the main goals of compounding. High temperatures damage the material just as much as extreme shear, caused by high rotational speeds, does. Fast melting sometimes leads to overstressing the material, which is why a gentle melting zone is preferred. But gentle melting means a longer melting zone and therefore longer extruders, which need more space and energy. This conflict leads to the need of a model to predict the exact energy input for one single element in the current process to find a compromise between both factors. The specific energy input can be described as power per mass flow, which in turn can be simplified to torque and speed of the extruder. At present, no models are available to describe the torque along the extruder with sufficient accuracy. In the present paper a new approach will be carried out, to calculate the determination of energy input by calculating the resistance of the plastic against the screws, causing a torsion of the screws. This can be repatriated to the torque and therefor the energy input. Different zones along the axis of the extruder are distinguished and described with different models. This results in a global model for the breakdown of the torque along the extruder. Basically, the flow of the individual zones is considered and the force of the melt flow on the screw surface is determined.

Compensation of Viscosity Fluctuations Through Local Temperature Adjustment in the Extrusion Die

Prof. Dr.-Ing. Christian Hopmann, Lisa Leuchtenberger, M.Sc., Malte Schön, M.Sc., March 2023

In recycling processes, different material streams with varying degrees of contamination and with different material properties are compounded into new granules. The varying material composition as well as the material degradation impact the melt viscosity from batch to batch. Different viscosities cause local different shear heating in extrusion dies such as binary pre-distributors. The resulting thermal inhomogeneities then lead to film thickness variations, which impair the film quality. In order to adapt the process to changing viscosities, e.g. when processing recyclates, one possible influencing variable is the die temperature in the vicinity of the flow channels. Current research of Hopmann et al. already addresses local cooling through the use of heat pipes in a binary pre-distributor for a low-density Polyethylene (PE-LD) [4]. The adaption of local heat pipe cooling to other materials with different rheological and thermal material properties is focused in this paper. In order to evaluate the applicability of the already developed system for other materials, computational fluid dynamics simulations (CFD-simulations) are carried out. The occurring inhomogeneities in temperature and velocity distribution with and without local heat pipe cooling are compared for PE-LD and a high-density Polyethylene (PE-HD). Finally, it is derermined whether the heat transfer capacities of the heat pipes specified by Hopmann et al. can be universally applied in addition to the existing heat pipe positioning.

New Polymer Processing Additives for Extrusion

Julia Resch, March 2023

Injection molding and extrusion are the two basic processing methods in plastics technology. The most likely applications, especially in case of extrusion, are in the packaging sector. Here, polyolefins represent the most important type of plastic in terms of volume. Worldwide, the total consumption of polyolefins is estimated at over 150 million tons per year. However, during the extrusion of polyolefins such as polyethylene or polypropylene, flow instabilities can occur as soon as the mass flow exceeds a critical value. These instabilities mainly show up in three different forms. They are referred to as the "sharkskin effect", the "stick-slip effect" and the "gross melt fracture". The effects mentioned appear with increasing shear rate. In the case of the sharkskin effect, periodic tearing of the product surface result, producing a sharkskin-like structure. The occurrence of the stick-slip effect manifests itself in products (for example, strands or films) with alternately smooth and jagged structures. If even higher shear rates during the process lead to melt fracture, in which the product undergoes extremely severe deformation. The flow instabilities can limit the production rate and thus also affect cost efficiency. To counteract them, processing aids such as fluoropolymers are added in practice. Fluoropolymers are considered to be extremely environmentally critical. Due to the strong C-F bond, these polymers can stay in the environment for an extremely long time and are therefore considered persistent. Due to this persistence, the introduction of the substances into the environment is so to say irreversible and poses a serious risk to humans and the environment. For this reason, their ban is often called for. In the past, various alternatives have been developed as polymer processing aids. Most of them are based on siloxanes. However, siloxanes also have high stabilities and durabilities, which is why they might be harmful to the environment, as well. The aim of this study therefore was to develop a novel and highly effective processing aid master batch that does neither pose a risk to health nor the environment. The new developed master batch should suppress flow instabilities and thus shift the process towards higher mass throughputs. In order to investigate the effectiveness of the processing aid master batches developed, they were tested with a high-pressure capillary rheometer. The capillary rheometer offers to detect the resulting pressure fluctuations, which are caused by the flow instabilities. For this purpose, a novel and high-resolution measuring nozzle was developed. This nozzle allows complete characterization and high-precision testing of the efficiency of the developed master batches. Various additives were tested. It was shown that the measuring method is suitable for detecting and accurately analyzing flow instabilities in the nozzle. It was also found that polysorbate is suitable as a flow aid for polyolefin extrusion. By adding polysorbate, the pressure instabilities could be suppressed so that surface effects were no longer visible.

The Benefits of Screw Cooling for Improved Solids Conveying for Smooth-Bore, Single-Screw Extruders

Timothy W. Womer, Mark A. Spalding, March 2023

All sections of a single-screw extruder must be operating well to maintain the maximum profitability of the line. The solids conveying section must be able to operate at a rate high enough to keep the metering section full of resin and pressurized. Optimal solids conveying depends on the forwarding and retarding forces on the solid bed, and these forces depend on the barrel and screw temperatures. Usually, a considerable level of care is given to setting the barrel and feed casing temperatures. The temperature of the screw, however, is typically not controlled. Instead, the screw temperature is unknown and often hotter than optimal. Screw cooling can improve solids conveying for many processes. This paper discusses the fundamentals and operational practices for using screw cooling.

Development of a New Granulate Feeding System for Gas Pressurized Extrusion Processes

Vitus Zenz, Dirk Muscat, Nicole Strubbe, March 2023

One major problem of a continuous process like plastic extrusion is their incapability to deal with non-local gas pressure. This is an inherent problem because a continuous process has an open end in the feeding port where pressure can escape. In this study a novel feeding system was developed to enable granulate feeding into gas pressurized processes inside a single- or twin-screw extruder. With this apparatus, gas pressure can be applied inside the extrusion process. The apparatus separates the pressurized extruder from the main feeder of the extruder. It keeps the pressure inside the system while continuously feeding new material into the process. The invention was patented under EP4130532 on 8th of February 2023 [1]. The apparatus was able to handle small amounts of granulates of around 100 - 200 g/h. An applied gas pressure of 0,8 MPa was achieved, with future optimizations aiming towards 1,5 - 2,0 MPa.

Challenges in Precision Molding and Emerging Micro Advancements

Donna Bibber, March 2023

Today, the need for micro molding applications is rapidly increasing, in part due to advancements in technology and scientific research. With medical devices becoming less invasive, and portable/wearable health devices gaining popularity, the need for small and highly precise components and parts has increased. Micro molding can be used to manufacture parts for these devices meeting the need for fine features, thin walls, micro holes, tight tolerances and scalability to high volume production. However, there are many challenges in molding micro parts or molding micro features and/or tight tolerances on small parts. This presentation will review some of the challenges involved in molding micro parts, sharps, micro holes, thin walls and micro automated assemblies and solutions for overcoming these challenges including resin selection and precision tooling considerations. In addition, we will discuss emerging applications where these micro advancements are required, including specialty surfaces.

Improved Injection Molding Warp Predictions by Characterization of Material Properties Using Measured Shrinkage Molding Data

Franco S Costa, Alexander Bakharev, Zhongshuang Yuan, Jin Wang, March 2023

A key objective of process simulation of thermoplastic injection molding is the accurate prediction of the final part shape after the part is ejected from the mold. Deviation of the molded part shape from the intended design is known as warp. In this research, we present a method to improve the accuracy of warp prediction by using calibrated mechanical properties. The calibrated mechanical properties can be the modulus, Poisson’s Ratio and Coefficient of Thermal Expansion. The calibration is achieved using a database of measured shrinkage molding data from a series of moldings of standardized test plaques having a variety of molding thicknesses and using a variety of process condition settings (packing pressure, melt temperature and injection velocity). Presented in this work are comparisons to actual molding data of final part shape predictions for both unfilled polymers and fiber reinforced polymer composites performed both with and without the shrinkage test calibrated mechanical properties. This includes a thin-walled part for which a post-molding buckling response is correctly predicted when the calibrated material properties are used.

Compensation of Batch Fluctuations of Post-Consumer Recyclate in Injection Molding by Phase-Unifying Process Control

Katharina Hornberg, Christian Hopmann, Marko Vukovic, Sebastian Stemmler, Dirk Abel, March 2023

The processing of post-consumer recyclates (PCR) causes fluctuating process conditions due to a varying composition and history of material batches. The fluctuations in part quality grow with increasing recyclate use and lead to reduced mechanical and optical part properties. We developed a phase-unifying process control approach, which combines injection and holding pressure phase by eliminating the switchover point. The approach realizes a given cavity pressure curve by adjusting the screw velocity in real-time. For calculation of the cavity pressure reference, batch differences have to be detected during the process. The objective of presented research is an evaluation of batch differences by material characterization and process analysis to account for economic online process control. First, we characterized five different PCR batches to identify the material properties and find fast and cost-effective methods to initialize the controller setting, as controller parameters depend on the processed material. Afterwards, we performed injection molding trials for different process settings to detect batch changes in quality data and process data. The material characterization showed that the melt flow rate and anorganic content have the highest correlation to part weight. Furthermore, batch changes have been identified most effectively by characteristics of the pressure curves, which makes an online determination of material batch changes possible.

Injection Velocity Versus Pressure Control During Filling

Bradley Johnson, March 2023

Capability studies were performed which compared the use of pressure-controlled and velocity-controlled filling of injection molded parts with plastic that had varying viscosity. A nozzle pressure transducer was used to control the pressure-controlled filling, as well as the pack and hold for both processes. Several methods of transferring from filling to packing were also compared which included screw position, cavity pressure sensors, and in-mold melt switches. This presentation will summarize the results of these studies.

Identification and Compensation of Part Warpage in Injection Molding Using On-Cavity Thermally Sprayed Heating Coatings

Christian Hopmann, Cemi Emre Kahve, Daniel Colin Fritsche, Theresa Kassel, Kirsten Bobzin, Hendrik Heinemann, Marvin Erck, Carsten Vogels, March 2023

Dimensional accuracy is – to this day – a challenging key quality aspect for manufactured parts using primary shaping processes. Many high-precision parts undergo multiple correction loops during the mold-making process to meet the required geometric tolerances. Each iteration not only increases production costs, but also requires additional human and environmental resources. Dimensional inaccuracies are caused by the process-based part deformation, a superposition of the phenomena shrinkage and warpage. The mechanisms for shrinkage and warpage a re strongly rela ted to the dependence of a polymer’s specific volume on pressure, and temperature (pvT-behavior). Shrinkage is inevitable, since it is caused by the continuous decrease in melt temperature and the inherent crystallization process during solidification. Warpage results due to local and timedependent variations of the melt temperature, cooling rate, and pressure and thus the local specific volume. These variations cause inner stress distributions that ultimately cause the part to warp. In order to reduce part warpage, the volumetric shrinkage must be homogenized across the part, which can be realized by manipulating the part's temperature locally and time dependent. In this work, the warpage of a box-shaped geometry with the material Polyoxymethylene (POM) is aimed to be reduced using a novel on-cavity thermally sprayed heating coating system. Two different heating coating systems are implemented and tested using commercial simulation and 3D-inspection software. For each system, the power level was varied, and the warpage was evaluated. In total, a warpage reduction of factor eight has been achieved using the heating coating.

The Universal Process: A Novel Approach to Enable Injection Molded Part(s) to Generate a Singular Process Across Multiple Machines and Materials

Lexington Peterson, Brandon Birchmeier, March 2023

The macro-issues the plastics industry is trying to resolve today pertain to sustainability, supply chain shortages, and the lack of skilled labor. Within the injection molding sector, manufacturers typically perform a full validation when a mold is moved to a different injection molding machine (IMM) or there is a material change. These full validations are labor-intensive, expensive, and not sustainable. Moreover, these methods may or may not utilize scientific molding principles. There has been a demand for a standard “part process” development method to transfer a mold between IMMs that is more efficient and can embrace variation in resins. iMFLUX’s Auto-Viscosity Adjust (AVA) technology has made doing so easier with its low, constant pressure injection molding process. This adaptive technology enables the molding process to automatically adjust parameters in real-time around parts’ response. This research focuses on developing a regenerative part process with low, constant pressure that is independent of resin and machine. Using AVA and cavity pressure sensors, two molds’ processes were transferred to another capable press with the original process, no user adjustments, and parts were studied for visual and dimensional integrity. It was determined that iMFLUX can automatically regenerate optimized part processes in different IMMs deemed capable with negligible part variation as seen from the visual and dimensional results. This is the first time an intelligent controller can autonomously redevelop and validate a part process to mold parts within spec despite varying IMMs and resins.

Thick Foam Molding for Sustainable Engineering Applications

Alicyn Rhoades, Ph.D., March 2023

Glass-filled engineering polymers are a staple in the automotive, aerospace, and medical industries. However, understanding the influence of glass fibers on the crystallization behaviors of these polymers is not trivial - especially at heating and cooling rates encountered during melt processing. Sample preparation plays an important role in the success of thermal analysis. For conventional differential scanning calorimetry (DSC) characterization, larger sample size is often used for composites than neat resin to mitigate the impact of possible filler inhomogeneity. Because the sample is quite small (thickness of less than 20 µm and a mass of less than 1000 ng) for fast scanning calorimetry (FSC), the potential impact of sample preparation on data reliability is very significant. In this study we explore and quantify the effect of sample preparation for DSC and FSC on crystallization kinetics in a wide temperature range using three grades, Poly(ether ether ketone) (PEEK), and its glass-fiber-filled composites (PEEK with 15 wt% and 30 wt% glass fiber) were studied, and X-ray computed tomography (XCT) with an ultrahigh-resolution was performed to reconstruct the interior structure of the composite pellets. When the sample thicknesses is less than 50 µm, the PEEK sample sliced perpendicular to the fiber flow direction have good filler homogeneity and always have a lower coefficient of variation than pellets sliced along the fiber flow direction. Furthermore, the data collected by both FSC and DSC were fitted and showed that FSC and DSC analysis can be reasonably used to predict the kinetics of composite materials.

Gas-Assisted Push-Pull: A Novel Technology to Significantly Increase Weld Lines Mechanical Performance

Marco Sorgato, Ph.D., March 2023

In injection molding of fiber-reinforced thermoplastics, in the presence of physical obstacles, such as cores, or for geometries that require multiple gates, weld lines develop where flow fronts rejoin. Regions affected by the presence of weld surfaces show worse mechanical properties. In fact, these areas are characterized by incomplete welding between the flow fronts and the presence of undesirable inclusions and porosity. In addition, due to the fountain-like flow in cavities, fibers on the weld line are unfavorably arranged and are unable to reorient themselves in the flow direction. If the incident flow fronts exhibit no pressure gradient during the defect formation and the holding phase, the morphology of the weld surface remains unchanged until the end of the process. By inducing a pressure imbalance after the formation of the weld line, on the other hand, it is possible to promote the interpenetration of one front into the other and significantly modify the local morphology. A dynamic packing stage during the first part of the holding phase therefore allows for improved matrix interdiffusion at the interface and fibers reorientation in the flow direction. Gas Assisted Injection Molding (GAPP) is a novel technology that allows for the dynamic packing of weld lines using only a single injection unit. Thanks to miniaturized gas injectors, it is possible to manipulate the molten polymer in the cavity and generate a flow through the weld surface. The dynamic packing achieved using GAPP allows for the elimination of weld lines in the core layer of the molded part, significantly increasing its mechanical performance. For a 35% glass fiber reinforced polypropylene, an increase in tensile strength and stiffness of 240% and 21.5%, respectively, can be observed in the defect region. GAPP can be implemented to solve weld line strength problems in all parts made of fiber-reinforced thermoplastics that require high mechanical performance, such as supports, brackets, cooling fans, pulleys, and other structural parts.

Practical and Simulative Investigation of the Influence of Surface Roughness on the Flow Path Length in the Injection Moulding Process

Prof. Dr.-Ing. Christian Hopmann, Moritz Mascher, M.Sc. RWTH, Christoph Zimmermann, M.Sc., March 2023

In this work, a practical and simulative study of the surface roughness of the injection mold cavity and the corresponding heat transfer between the plastic melt and the mold cavity was conducted. The work shows that slightly longer flow paths can be achieved through the rougher mold surface, which indicates to a lower heat transfer. However, the influence is small compared to other influences such as the used molding compound or the injection pressure. An analysis of the structural replica over the flow path shows a clear decrease in the structural height towards the end of the flow path, i.e. with decreasing pressure and lower melt temperature. To describe the influence of microstructures on the heat transfer using injection molding simulations accurately, a model calibration is used.

Advances in Material Testing for Injection Molding Simulation

Russell G. Speight, Paul A. Brincat, Vishak D. Perumal, March 2023

Injection molding simula tion bega n in the 1970’s, with technology advancing to the present day. In parallel, material testing technology has evolved over the same period, to meet the increased accuracy demands of simulation. The accuracy of injection molding simulation is influenced by many factors such as: (i) modeling of part geometry, (ii) runner and nozzle, (iii) mesh type and density, (iv) mathematical finite element solution, (v) injection molding machine process settings, and (vi) material data. The focus of this paper is material data, from the latest material testing methodologies, outlining technological evolution to meet the demands of the highest accuracy simulation.

Effect of Reactive and Non-Reactive Coupling Agents on Mechanical Properties of Recycled Polyethylene Terephthalate Filled With Ground Tire Powder

Aboulfazl Barati, March 2023

Accumulated used polymers and tires cause several ecosystem issues in landfills. A practical method was proposed to reuse recycled polyethylene terephthalate (rPET) and ground tire rubber (GTR) powder by melt composite process. A composite material was developed in this work using GTR for reinforcement and rPET for matrix. The effect of two non-reactive (styrene-butadiene-styrene (SBS) and styrene-ethylene-butadiene-styrene (SEBS)) and three reactive (ethylene-methyl acrylate-glycidyl methacrylate (EMA-GMA), ethylene-glycidyl methacrylate (EGMA) and SEBS grafted with maleic anhydride (SEBS-g-MA) coupling agents on the mechanical properties of the composite material were evaluated. Mechanical tensile and impact strength properties were evaluated to determine how coupling agents affect composite behavior. All reactive coupling agents improve the mechanical behavior of composite materials, whereas non-reactive ones have little effect. EMA-GMA and EGMA are more reactive with rPET than SEBS-g-MA. Using 10 wt% of EMA-GMA in the composite of rPET/GTR (4:1) increases the tensile strain and impact strength (950% and 23%, respectively) and decreases maximum tensile strength and Young’s modulus (16% and 35%, respectively).