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
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.
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.
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