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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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Additive Manufacturing & 3D Printing

Reduce Costs and Lead Times for Large Thermoforming Molds with EXT Titan Pellet 3D Printers
Bradley Mount, Clay Guillory, Dave Rheinheimer, Nino Pecina, October 2023

To address growing supply chain pressures, manufacturers are turning to Additive Manufacturing (AM) to create quality, cost-efficient products faster. Plastic thermoforming companies like Duo Form have discovered how to leverage large-format extrusion 3D printing using low-cost plastic pellets to gain a competitive edge. They are producing medium-to-large-sized thermoforming molds in less than half the time, and at a fraction of the cost compared to traditional mold-making methods. Join engineering and business experts from 3D Systems and Duo Form as we dive deep into the integration that has made pellet-extrusion AM so beneficial for Duo Form, and how you can reap the same benefits in your own thermoforming processes. In this webinar, you will learn about:

  • How pellet extrusion 3D printing enables up to 10X faster mold print times, with up to 10X savings on material costs
  • Duo Form specific case studies and ROI examples
  • Best practices that improve AM production and mold performance such as: how to print porous molds, using infill settings for improved vacuum pulls, and more!
  • How to select the right AM solution, including options for additive-subtractive hybrid systems

3 Challenges of Testing Plastics
Sammi Sadler & Stephanie Williams, September 2023

The world of plastics is constantly evolving, with new applications such as high-performance polymers, additive manufacturing, and bioplastics continually emerging to transform the field. Common to all applications - old and new - is the importance of mechanical testing that ensures manufacturers are producing quality products. In this webinar we'll be discussing the specific challenges of testing plastics, the importance of repeatable and reliable mechanical testing results, and what you can do to improve your results. Topics

  • Overview of recent changes in key standards
  • Factors that influence test results – solutions and troubleshooting tips
  • How to increase laboratory efficiency and throughput to improve test times

An Overview of Additive Manufacturing: Exploring the 7 Families of 3D Printing Technologies
Elliot Saldukaite, July 2023

Join us for an insightful and educational webinar as we delve into the captivating world of additive manufacturing! Through its partnership with 3Dnatives, the largest online media platform for 3D printing, SPE is proud to present this webinar dedicated to additive manufacturing. It will take place on July 12th 11AM ET and will be made available free of charge exclusively to members of SPE.

Embark on a comprehensive journey through the seven families of 3D printing technologies while gaining an understanding on the landscape of the 3D printing industry. With a focus on the current state of additive manufacturing, we will explore the latest advancements, trends, and innovations that are shaping the industry and may define the future of additive manufacturing.

Led by Elliot Saldukaite, 3Dnatives' passionate Technical & Digital Content Specialist, this webinar aims to equip members with a solid foundation on the topic of 3D printing, enabling them to further explore the dynamic world of additive manufacturing as well as understand the processes and solutions available.

Don't miss out on this opportunity to broaden your horizons and discover the potential of additive manufacturing. Reserve your spot today and join us on July 12th to discover more about 3D printing and how it can affect the plastic and manufacturing industries.

Build A Strong Business Case for Bringing 3D Printing Into Your Plastic Injection Operations
Arnaud Divialle, June 2023

3D printing holds great promise for manufacturing. And yet, deployment and adoption has lagged. One reason for this appears to be that building the business case for 3D printing is a major roadblock for many companies. Join us to find out why building your business case is critical to successfully using 3D printing in plastic injection, and to learn how to build robust justifications for investing in 3D printing by:

  • Identifying which parts would benefit from 3D printing, and how
  • Estimating the reduction in cycle time achievable with 3D-printed inserts/li>
  • Reducing uncertainty about the benefits of 3D printing/li>
In this webinar, we discuss the challenges of adopting 3D printing in the plastic injection industry and share our in-depth knowledge of powerful solutions you can use to make the best investment decisions. You'll learn:
  • How front-end simulation can help you make better decisions faster
  • What to consider in your decisions
  • Strategies for reducing the risk of adopting new technologies

Large Thermoforming Molds Twice as Fast for Half the Cost
Bradley Mount, Clay Guillory, Dave Rheinheimer, Nino Pecina, April 2023

To address growing supply chain pressures, manufacturers are turning to Additive Manufacturing (AM) to create quality, cost-efficient products faster. Plastic thermoforming companies like Duo Form have discovered how to leverage large-format extrusion 3D printing using low-cost plastic pellets to gain a competitive edge. They are producing medium-to-large-sized thermoforming molds in less than half the time, and at a fraction of the cost compared to traditional mold-making methods.

Join engineering and business experts from 3D Systems and Duo Form as we dive deep into the integration that has made pellet-extrusion AM so beneficial for Duo Form, and how you can reap the same benefits in your own thermoforming processes.

In this webinar, you will learn about:

  • How pellet extrusion 3D printing enables up to 10X faster mold print times, with up to 10X savings on material costs
  • Duo Form specific case studies and ROI examples
  • Best practices that improve AM production and mold performance such as: how to print porous molds, using infill settings for improved vacuum pulls, and more!
  • How to select the right AM solution, including options for additive-subtractive hybrid systems

Digitalization — The Key for Circularity in Plastics
Christian Hopmann, Ph.D., March 2023


  • Production, usage and lifetime of plastics by industry sector
  • Circular economy
  • The reality 2018: global plastics flow
  • Options for plastics waste utilization
  • Key action areas for sustainable use of plastics
  • Ingredients of a nominal PE-LD recyclate
  • Recycled material based on post-consumer packaging waste
  • Value chain of packaging products
  • DPP – Digital Product Passport
  • Information exchange concept
  • Scaling to industrial level
  • AI based process model based on synthetic data
  • Digitalization as enabler for robust and sustainable production
  • Holistic approach towards a circular plastics packaging economy
  • Conclusion and Outlook

Compressive Behavior of Additively Manufactured Elastomeric Thermoplastic Polyurethane Honeycomb Structures With 2D Density Gradients
Mohammad Ahmed, March 2023

The advent of additive manufacturing (AM) brought in new dimensions to the research and development efforts of cellular polymeric structures by offering design freedom, resulting in tailorable architected structures optimized for specific applications. This work proposes a two-dimensional (2D) density gradient approach to design graded honeycomb structures for energy absorption applications. Graded honeycomb structures having three levels of density gradients (low, medium, and high) and their uniform density honeycomb equivalents were manufactured using material extrusion (MatEx) based fused filament fabrication (FFF) AM process. The material used for the FFF process was thermoplastic polyurethane (TPU) elastomer (Polyflex). The relative density of the structures was in the range of 0.259 – 0.346. A comparative study of the compressive behavior of the graded and regular honeycomb structures was carried out using in-plane quasi-static compression tests. Unlike regular honeycomb structures, all the graded honeycombs showed gradual stepwise deformation. Compared to their honeycomb equivalent counterparts, the high gradient honeycomb showed significantly different force-displacement profile compared to medium and low gradient honeycombs. While high gradient honeycomb showed higher maximum crushing force compared to the honeycomb equivalent, medium and low gradient honeycombs showed higher crush force efficiency. The experimental results were evaluated and compared with non-linear finite element analysis (FEA) simulation results. The hyperelastic properties of the TPU material were defined using Mooney-Rivlin constitutive model. The simulation results agreed well with the experimental results. The proposed 2D gradient parametric design methodology, coupled with the experimental and simulation results, can broaden the knowledgebase of graded honeycomb design principles, thus providing unique opportunities to develop and improve additively manufactured light-weight structures for commercial applications, ranging from automotive and transportation to healthcare and consumer products.

Comparison of the Anisotropy of the Mechanical Properties of Injection Moulded and Additively Manufactured Parts
Johannes Austermann, Rainer Dahlmann, March 2023

Based on its mouldless, layer-wise manufacturing principle, screw-extrusion-based Additive Manufacturing (AM) allows for the efficient and economical production of thermoplastic prototype parts. During manufacturing, thermoplastic pellets are molten in a single-screw extruder and discharged through a nozzle. As the extruder is moved by a kinematic, the melt is subsequently locally discharged in a strand- and layer wise fashion to successively build up a part, similar to established AM processes such as the Fused Filament Fabrication (FFF). In contrast to FFF, standard thermoplastic pellets can be processed, as a single-screw extruder instead of a heated nozzle is used for plasticising the material. Thus, enabling injection moulding (IM) prototypes to be manufactured from series IM grade materials, including filled materials such as talc-filled polypropylene. However, the layer-wise additive manufacturing leads to anisotropic mechanical part properties in terms of strength and stiffness, which differ from the properties of the final IM-part, currently limiting the use of AM-parts to concept- and geometric-prototypes. These properties not only result from lower part strength orthogonal to the direction of deposition due to incomplete healing between adjacent strands, but also from a difference in filler-orientations, based on the process specific flow behaviour of the melt during processing. To extend the use of parts manufactured in screw extrusion AM to functional- or even technical prototypes, for which the mechanical properties are crucial, an understanding of these differences in the anisotropic mechanical behaviour of AM- and IM-parts is necessary. In the scope of this work, the quasi-static tensile and flexural properties as well as the high-speed tensile properties of additively, screw-extrusion-based manufactured and injection moulded parts are investigated, taking into consideration differences in the filler orientation between the manufacturing processes. To account for the anisotropy, testing is performed in several directions relative to the direction of deposition in AM or the direction of flow in IM. Furthermore, optical investigations are performed to assess the impact of filler orientations. The investigations are performed by manufacturing 1BA tensile test specimens from a 20 wt.% talc filled IM grade polypropylene material in screw-based AM and IM, which are subsequently used to perform quasi-static tensile and high-speed tensile testing. In addition, test specimens in accordance with DIN EN ISO 178 are manufactured for flexural testing. To allow for comparability, the test specimens are indirectly manufactured, i.e. both in AM as well as IM plate geometries are produced, from which the test specimens are milled. The AM parts are tested parallel and orthogonal to the strand-direction as well as at an angle of 30° and 60° relative to the strands. For IM, testing is carried out parallel and orthogonal to the direction of flow. In addition, µCT and microscopic investigations are conducted to analyse the orientation of the filler. While the results show an anisotropy in strength and stiffness for both IM and AM specimens, the anisotropy of these properties is significantly more pronounced in case of AM. This is based on the higher degree of filler orientation in the strands of the AM-parts. At the same time, only a partial orientation of the fillers in flow direction can be determined for IM-parts, showing that the fillers used can impact the comparability of AM and IM-prototypes. Additionally, it is shown that a higher comparability of the part properties is possible in the case of a quasi static load, compared to high-speeds of load application, limiting the use of AM-prototypes to such load cases.

Structure Development of Semi-Crystalline Polymers in Laser Based Powder Bed Fusion
Simon Cholewa,,reas Jaksch, Dietmar Drummer, March 2023

The impact of melt hardening at low melt undercooling and under atmospheric pressure creates boundary conditions that have yet to be extensively studied since traditional techniques do not require such information. However, for powder bed fusion of polymers, the transition from the melt after exposure to an elastically dominant melt is critical as the crystallization in the building phase occurs under these conditions yielding stresses due to crystallization volume shrinkage. As a result, a process-adapted evaluation is required to determine how long the molten polymer remains viscously dominant, and the point where the stresses are stored in the melt. Therefore, the crystallization of semi-crystalline melt is investigated in this work using rheological data in conjunction with FTIR microscopy. A modified measurement setup of the rheometer with an ATR crystal allows a simultaneous description of crystallization by FTIR spectroscopy and measurement of the rheological behavior of the material. A comparison between the different techniques indicates that the increase in viscoelastic properties during crystallization begins at low degrees of crystallinity. It is determined that the solidification of the melt is detectable at relatively low degrees of crystallization conversion and that no stresses are accumulated in the material until this point.

Numerical Simulation and Experimental Investigation of the Flow Behavior in Material Extrusion Additive Manufacturing
Julian Kattinger, March 2023

The melting of a plastic filament in an FFF extruder is characterized by the fact that there is hardly any frictional heating, and instead heat conduction and radiation between the nozzle wall and the filament plays the major role. Experiments have shown that these heat transfer mechanisms limit material heating and thus the overall production rate. For this reason, many efforts have been made to capture the melting behavior of the filament through analytical models, numerical simulations or experiments. This presentation focuses on a CFD simulation of non-Newtonian and non-isothermal polymer flow through the nozzle of a fused filament fabrication printer. The simulations were performed for a wide range of filament velocities at different nozzle temperatures and then compared with two different types of experiments. A comparison with experimentally measurements of the force required to push the filament through the nozzle showed that the assumptions used for the simulations are suitable to predict the melting and flow behavior in the relevant processing range. In addition, an experimental method was used to allow in-situ observation of melt flow in a printing nozzle using X-ray micro-computed tomography. In this way, it was possible to study the velocity distribution in the nozzle and to gain insights into the melting mechanism that can be used for future modeling approaches.

Strategic Cost and Sustainability Analyses of Injection Molding and Material Extrusion Additive Manufacturing
David Kazmer, Ph.D., March 2023

Economic and environmental costs are assessed for four different plastics manufacturing processes, including stock and upgraded material extrusion 3D printers, as well as cold and hot runner molding. Characterization indicated the larger stock 3D printer had a melting capacity of 14.4 ml/h while the smaller but upgraded printer had a melting capacity of 36 ml/h. 3D printing at these maximum melting capacities resulted in specific energy consumption (SEC) of 16.5 and 5.28 kWh/kg, respectively, with the latter value being less than 50% of the lowest values reported in the literature. Even so, analysis of these processes found them to be only 2.8 and 3.5% efficient, respectively, relative to theoretical minimum energy requirements. By comparison, all-electric injection molding with a cold runner mold had a specific energy consumption of 0.205 kWh/kg and was 54% efficient relative to the theoretical minima. Breakeven analyses considering the cost and carbon footprint of mold tooling found injection molding provided lower costs at a production quantity around 70,000 units and a lower carbon footprint at a production quantity around 10,000 units. Parametric analysis of model inputs indicates that the breakeven quantities are robust with respect to carbon tax incentives but highly dependent on mold costs, labor costs, and part size.

Effective Wall Thickness for Computational Modeling of Polymer Extrusion Additive Manufacturing
Saratchandra Kundurthi, Mahmoodul Haq, Abdifitah Adan, March 2023

Polymer material extrusion additive manufacturing processes like fused filament fabrication (FFF) are increasingly being used for structural applications. Accordingly, there is a growing need for computational modeling to characterize and predict the process output and printed part performance under load. Prior studies have shown that the modulus and strength in the build direction (Z-direction) are sensitive to the surface bead shapes and can vary extensively depending on the print settings used. This presents a challenge for part-level (macro-scale) finite element analysis (FEA) because the material properties required for such models can vary from part to part or even different locations within the same part. The use of stress concentration factors is a critical step in computing effective material properties to be used in macro-scale numerical models. However, theoretical stress concentration factors (kt) published in literature for material extrusion AM are limited to tensile loading only. In this work, we demonstrate how the kt from tensile loading can be extended to other load cases. Meso-scale FEA was used to perform parametric studies with varying bead shapes. The models were subjected to pure bending loads as well as bending loads combined with shear loads. The stress concentrations were then evaluated, but with multiple iterations of the wall thickness used for nominal stress calculations. The results were compared to the results from pure tensile loading, and it was observed that the choice of wall thickness is trivial for tensile loads but is critical for bending loads. An equation for effective wall thickness was derived that yields consistent stress concentration factors for any bead shape, irrespective of the applied load. The results were also compared with the effective wall thickness for calculating the Z-direction modulus as published in literature. Ultimately, separate recommendations for effective wall thickness are presented for calculating modulus, strength, and the actual geometry used in macro-scale FEA models.

Dry Stereolithography
Manilal J. Savla, March 2023

Dry stereolithography is a new and patented process that uses thermoplastic photopolymers in film or sheet/plate form instead of liquid photopolymer resins and does not require support structures during the build process. The process generally relates to the use of dry photopolymers to make a 3D printed object formed from individually and selectively exposed dry photopolymer layers of the same or gradually varying shape. Suggested markets for dry stereolithography are outlined. Photopolymer plates/sheets/films as raw materials are environmentally friendly.

Simultaneous Processing of Thermosets and Thermoplastics in Additive Manufacturing of Multi-Material Polymer Parts
Robert Setter, Katrin Wudy, March 2023

Multi-material additive manufacturing (AM) pushes the barriers of complex part production with a comprehensive and complementary material spectrum to unprecedented heights. The experimental “Fusion Jetting” technology is one of the first attempts to simultaneously process thermoplastics and thermosets within a single AM process to functional multimaterial parts. Applications lie in the field of load-path optimized reinforcements, hard-soft and smart structures as well as the strategic variation of the mechanical, thermal, and electro-magnetic part properties. This investigation focuses on the implementation of UV-curable acrylates within thermoplastic polyurethane (TPU) parts utilizing an experimental laser-based AM process to specifically alter the mechanical behavior of future parts. Process parameters like the laser power or the acrylate content within each plane are strategically varied to examine their respective impact on the mechanical and microscopic part properties. Based on tensile testing results, an increase of the Young’s Modulus for TPU parts with acrylate reinforcements is detected. The choice of the sequence of the individual process steps proofs fundamental towards the laser/material interaction and the infiltration behavior. This includes the detection of increased infiltration of the acrylate within melted regions of TPU using low energy densities resulting in parts with increased porosity. The results are further discussed towards the bonding behavior between the materials, including the potential impact of selected process parameters on the visually detected delamination behavior during mechanical testing.

Characterization of Polyaryletherketone (PAEK) Filaments and Printed Parts Produced by Extrusion-Based Additive Manufacturing
Manuel Garcia-Leiner, Benjamin Streifel, Steven M. Kurtz, Daniel W. MacDonald, Cemile Basgul, March 2023

This study describes a detailed analytical characterization of polyaryletherketone (PAEK) polymers used in extrusion-based additive manufacturing. The results provide key observations and highlight differences between commercially available polymers of the PAEK family, specifically polyetheretherketone (PEEK) and polyetherketoneketone (PEKK). Results suggest that inherent differences in their molecular structure led to notable differences in terms of their viscoelastic, thermal and physical properties. Similarly, direct comparison of the properties between parent filaments and three-dimensional printed (3DP) parts suggests that, as observed in subtractive processes, the molecular structure of the PAEK polymer selected (PEEK or PEKK), as well as the inherent physical properties associated with it, determine greatly the performance of final 3DP parts. Differential scanning calorimetry results suggest that the glass transition temperature (Tg) of PEEK 3DP bars (146.8 °C) is about 8 °C lower than that of the parent PEEK filament (154.8 °C). These small differences manifest greatly in the viscoelastic response after Tg, and the temperature at which a decrease in storage modulus is observed occurs consistently at lower temperatures in 3DP PEEK bars (ca 130 °C) compared to PEEK filaments (ca 150 °C). In contrast, no observable differences are noted between parent filaments and 3DP bars in PEKK polymers. For these polymers, the inherent semi-crystalline behavior dominates their thermal and viscoelastic response. These structure–property relationships provide fundamental understanding to aid in the design and manufacturing of several industrial and biomedical applications that could potentially leverage the advantages of high temperature thermoplastic PAEK resins, as well as in the incorporation of these polymers in a growing number of technologies encompassing the field of additive manufacturing.

Powder Bed Fusion of Polymer-Based Separators for Solid-State Batteries
Katrin Wudy, Ph.D., March 2023

Powder bed fusion of plastics has reached a high maturity level up to now and the technology is used for different applications in the area of transport, consumer goods and for medical applications. Having a look at the area of energy storage systems mainly metal additive manufacturing techniques are used. The is an increasing need for innovative storage technologies, such as solid-state batteries, as well as novel production technologies. In this paper, a novel approach to manufacturing the so-called polymer separators for solid-state batteries with powder bed fusion is represented. Two different potential candidates for the polymer materials for the separator are analysed regarding their process behaviour in powder bed fusion. PEO and PVDF are commonly applied as materials for the solid-state separator. Optimal process parameters for the manufacturing of PVDF and PEO with powder bed fusion process to generate homogenous and dense layers are the key findings of this paper and provide deepened process understanding. As a result, the first proof of concept for producing separator layers by printing in a scalable process is shown.

Let AI Run Your Compounding
Saeed Arabi, March 2023

Compounding is a science. It requires a great knowledge of Chemistry, Formulation, Processing, Equipment and the Human Factor and most recently Artificial Intelligence. Compounders of today are facing many challenges that their predecessors did not face. Market fluctuation due to global issues, labor shortages as a result of pandemic, force majeure by raw material producers are few of many challenges facing compounders now. Purpose of this presentation is to show how you can use AI in your compounding operation and potentially increase your efficiency by at least 25%.

A Digital Twin for Setup Time Reduction in Single-Screw Extrusion of PVC Tubes
Enrico Bovo, March 2023

A Digital Twin (DT) can be defined as a digital representation of an actual physical system, where the data flow between the virtual and the real entity is fully integrated in both directions. In this work, a soft-sensor-based DT was developed for the real-time monitoring of the most important quality indexes in the manufacturing of plastic extruded tubes, i.e. the weight per unit length and the inner diameter. An extensive experimental campaign was conducted on a real tube extrusion line using three polyvinyl-chlorides (PVC) and different process conditions, and machine learning regression algorithms were trained and tested to create the models of the extruder and the extrusion die, on which the DT is based. The characterization of the considered material, whose properties were given as input to the digital models, was carried out according to a procedure based only on the data collected by the production line. The DT was tested for the startup of the production of a single-layer tube, and allowed to achieve the specified customer requirements (thickness and weight) in few minutes. The proposed solution thus proved to be a useful tool for reducing the setup time, thus increasing the efficiency of the process.

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.

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.

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