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.
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.
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.
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.
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 polyamide 11/carbon black (PA11/CB) SLS nanocomposite printing powder was characterized throughout a laser area energy density range (express by using Andrew’s numbers, AN) to elucidate significant changes to the PA11 microstructure and chemistry during the SLS printing process. We will show that there are specific microstructural changes that occur in PA11, some gradual and others more striking, between the as received PA11/CB powder and printed parts. The melting temperature (Tm), percent crystallinity (Xc), lamellae thickness (lc) and dhkl spacing of PA11 were all shown to change significantly upon printing, whereas the molecular weight was shown to have a rather gradual increase as a function of AN. These results imply that the printing conditions used result in an irreversible change in PA11 polymer microstructure and chemistry, and correlate well to the measured mechanical behavior of parts print with corresponding AN. The use of DSC, XRD, and molecular weight analysis provides a more complete picture of the changes due to the SLS printing process and can help optimize the printing parameters to create high-quality printed parts.
Robotic 3D printing systems utilizing photopolymers can enable free-standing structures, large-scale printing, extensive mobility, and increased part complexity. However, to better estimate robotic printing parameters and eliminate expensive trial-and-error approaches, a simulation framework for curing behavior is needed. In this work, an autocatalytic curing model, considering printing speed, UV light intensity, spotlight diameter, and filament thickness, was used to create a MATLAB simulation to study the effect of different printing parameters. The printed filament was discretized into a set number of elements over its length and thickness. UV light exposure time above each element was derived based on spot diameter and printing speed. This simulation framework, combined with experimental data (real-time ATR-FTIR), can better inform decisions regarding printing parameters selection. Overall, it was estimated that a speed ≤ 3 mm/s with a filament thickness ≤ 2 mm would produce acceptable ranges of degrees of cure at different UV light intensities and spot diameters. Finally, control of printing parameters (robotic arm movement and UV light intensity) to obtain a specific degree of cure (DoC) ensuring structural rigidity is demonstrated for a two-degree-of-freedom manipulator, showing both the desired endeffector position and the desired DoC are achieved in four seconds.
Fused Deposition Modelling (FDM) technology is a widely used additive manufacturing processes. In this process, a plastic filament is fed to a nozzle, melted there and deposited in the X, Y direction based on an imported geometry. Afterwards the print bed moves one layer in the Z direction and starts depositing the plastic again in the X, Y-direction. These steps are repeated until the component is completely built up. In a recently developed system by one of the authors, the degrees of freedom in movement of the print head are extended to five axes: X, Y, Z-movement in translational direction plus an additional degree of rotation of the print bed and the possibility to tilt the print head with respect to the printed surface. Thereby, the surface quality and the geometric accuracy for rotationally symmetrical parts are intended to be improved. This paper investigates the potential of the additional motion axes with respect to part quality. To determine the accuracy, surface quality and the ability to print overhangs, tests have been carried out and compared to conventional manufactured FDM parts (X, Y, Z-kinematics). In a further step, the printing of the parts after model preparation in polar coordinates is compared to printing in Cartesian coordinates. To investigate the influence of the print head adjustment on part quality, namely surface roughness, test runs were performed with print head adjusted at different angles to the surface. Suitable demonstrators were developed for this purpose and evaluated in comparison with manufactured FDM parts using commercially available printers limited to X, Y, Z-movement only. The tests show that the recently developed 5-axis printer has a lot of potential. It’s comparable in performance to a commercially available FDM printer from the mid-price segment. The possibility of tilting the print head is the biggest advantage of the system. This has significantly improved part quality when printing overhangs and angled surfaces. The comparison between polar and Cartesian coordinates showed an improvement in surface quality for cylindrical parts printed by polar coordinates.
The purpose of this study was to investigate the feasibility of in-situ foaming in fused filament fabrication (FFF) process. Development of unexpanded filaments loaded with thermally expandable microspheres, TEM is reported as a feedstock for in-situ foam printing. Four different material compositions, i.e., two grades of polylactic acid, PLA, and two plasticizers (polyethylene glycol, PEG, and triethyl citrate, TEC) were examined. PLA, TEM and plasticizer were dry blended and fed into the extruder. The filaments were then extruded at the lowest possible barrel temperatures, collected by a filament winder, and used for FFF printing process. The results showed that PLA Ingeo 4043D (MFR=6 g/10min) provides a more favorable temperature window for the suppression of TEM expansion during extrusion process, compared to PLA Ingeo 3052D (MFR=14 g/10min). TEC plasticizer was also found to effectively lower the process temperatures without adversely interacting with the TEM particles. Consequently, unexpanded filaments of PLA4043D/TEM5%/TEC2% was successfully fabricated with a density value of 1.16 g/cm3, which is only ~4.5% lower than the theoretical density value. The in-situ foaming in FFF process was then successfully demonstrated. The printed foams revealed a uniform cellular structure, reproducible dimensions, as well as less print marks on the surface, compared to the solid counterparts.
This paper describes the development of innovative temperature control concepts for use in additively manufactured inserts based on CO2. These have been successfully investigated for their suitability in small batch production. The additive manufacturing processes have been evaluated in terms of their suitability for the production of mold inserts. It has been possible to reduce the time required to prepare the inserts. In the investigation of suitable plastics, POM has proven to be suitable. Of the generative manufacturing processes investigated, stereolithography was found to be suitable. Robust manufacturing in the injection molding process with the other additive manufacturing processes was not possible. The manufactured components were examined with regard to their properties and compared with conventionally injection-molded components. It was found that a clear dependence on the manufacturing process of the insert used for production can be observed, especially in the crystalline microstructure of the manufactured components. This makes it difficult to use additively manufactured tool inserts in small-batch production, since the resulting properties of the components in terms of crystallinity and thus distortion are not comparable with injection-molded components. In further investigations, the minimum necessary thermal properties of the printing materials must be determined in order to ensure robust small series production with component crystallinity comparable to the injection molding process.
In Spring of 2020, Instaversal was contracted to test our newly developed conformal cooling technology, CoolTool™, against existing production benchmarks for a plastic injection molded Pipe Bracket Adapter. The Product Innovator was going through a period of elevated demand where the current cycle time of the existing injection mold tool prohibited them from meeting their demand. When cooling cycles were sped up this led to higher scrap rates due to sink marks. This left the Product Innovator with two options: delay delivery of the product to their top customer with the risk of losing the sale and potentially losing the customer or to invest in additional injection mold tools to double production capacity. To meet the customer’s demand, 100,000 parts needed to be produced in a 60-day time period. This request created conflict with the contract manufacturer. They were being asked to absorb the cost of additional molds to meet the timing or run full 24-hour (Monday-Friday) shifts over the 60-day period which would create losses in revenue by eliminating other clients’ scheduled jobs.
Functionally gradient 3d printing is of great importance for polymer composites to be applied in soft robotics or smart electronic devices. Imparting mechanical gradients within the design of new materials would help to prevent premature failure of devices and could reduce strain mismatches. In this work, we first focus on investigating the mechanical gradients and water responsive behavior of cellulose nanocrystal (CNC) / thermoplastic polyurethane (TPU) films by changing the concentration of CNCs. After generating masterbatched feedstocks, CNC/TPU films were extruded with a single screw extruder to obtain 3D printable filaments. The thermal and rheological behavior of the nanocomposite system is characterized to evaluate the mechanical property gradient of CNC/TPU filaments as a function of CNC concentration within a 3D printed geometry.
Recognized over the years for its exceptional prototyping quality and part accuracy, SLA-based additive manufacturing is changing in a big way, with an automation-ready solution that offers up to twice the print speed and up to three times the throughput of existing SLA systems. Join us as we reveal the revolutionary innovations that we are introducing with our new SLA 750 full workflow solution. Providing breakthrough gains in speed, throughput, material performance, and cost-efficiency for factory-floor production, this complete solution features production-grade materials, automation compatibility, and AI-based seamless integration with all factory floor equipment. These innovations now more effectively answer your requirements, from prototyping to production, whether you are a service bureau, automotive, aerospace, consumer goods, foundry or medical device manufacturer.
A significant problem associated with repairing deteriorating highway culverts is the resultant lowered flow capacity. This can be mitigated by the use of culvert diffusers. Current culvert diffusers are made using fiberglass reinforced thermosetting epoxy polymers, which require custom made molds. This research work explores the use of large-scale 3D printed thermoplastic polymer composite to manufacture culvert diffusers. The research work shows that 3D printing technology reduces the manufacturing time as well as the cost of culvert diffusers. Large-scale 3D printing technology is well-suited for the manufacture of individualized culvert diffusers with unique geometrical designs without the need for molds. 3D printing technology is also capable of using different materials according to environmental requirements. The use of segmental manufacturing in conjunction with large-scale 3D printing enables the manufacturing of culvert diffusers larger than the build envelope of the 3D printer. Different post-processing techniques used for cutting, finishing, and joining the 3D printed segments are discussed.
Fused Deposition Modeling (FDM) parts generally show a fluid permeability due to their specific and characteristic strand structure. Therefore, an application including contact with water is difficult and limits the areas of application of this Additive Manufacturing (AM) technology. In this paper the aim is to determine the water tightness of FDM manufactured Ultem 9085 structures in a pressurized system using a suitable test setup. Based on the results, optimization approaches such as parameter modification, variation of the specific part thickness and a surface treatment shall identify if a complete tightness can be realized. For the validation of the results, analysis methods such as CT-scans and macroscopic images are used to determine the component surface.
This study reports the effect of carbon fiber (CF) on the fracture toughness of 3D printed carbon fiber/ acrylonitrile butadiene styrene (CF/ABS) composites. Chopped carbon fiber was compounded with ABS to prepare CF/ABS filaments containing 0-25 wt.% CF. Compact tension specimens were designed, 3D printed, and tested to measure the composites’ mode-I fracture toughness, KIc. The results showed CF/ABS composites can be made with up to 25 wt.% loading without any drop in their fracture toughness. In fact, ABS’s KIc increased by ~22% with an introduction of 10 wt.% CF. There was a slight drop in KIc, once the CF content was increased to 15 wt.%. Further increase in CF content from 15 to 25 wt.% did not cause any significant change in KIc and it was found to remain similar to that of the neat ABS. The fracture toughness trend with CF content was qualitatively explained in terms of two competing mechanisms, namely increased actual fracture surface area and less perfect interlayer adhesion at the presence of CFs.
Additive manufacturing of stimuli-responsive materials is an area of 4D-printing that is continuing to gain interest. Cellulose nanocrystal (CNC) thermoplastic nanocomposites have been demonstrated as a water responsive, mechanically adaptive material that has promise to generate 4D-printed structures. In this study, a 10wt% CNC thermoplastic polyurethane (TPU) nanocomposite is produced through a masterbatching process and printed using fused filament fabrication (FFF). A design of experiments (DOE) was implemented to establish a processing window to highlight the effects of thermal energy input on printed part mechanical adaptivity (dry vs. wet storage modulus). The combination of high temperatures and low speeds result in thermal energies that induce significant degradation of the CNC/TPU network and reduced absolute values of storage moduli, but the mechanical adaptation persisted for all the printed samples.
Additive manufacturing (AM) of polyolefins, such as polypropylene (PP), employing filament-based material extrusion (MatEx) has gained significant research interest in recent years. The semicrystalline nature of PP makes it challenging to process using MatEx. The addition of amorphous low molecular weight hydrocarbon resins into PP matrix was found to delay the onset of crystallization of the blends. The slow crystallization behavior, as evident by the increased crystallization half-times, aided the relaxation of residual stresses during MatEx of PP blends that resulted in manufactured parts with reduced warpage. Rheological characterizations were performed on the PP blends revealing the shear-thinning nature. The combined interaction among crystallization rates, timescales, and morphology was found to affect the interlayer welding process during MatEx. Mild thermal annealing of the manufactured parts resulted in mechanical properties which approach that of injection molded parts.
Additive manufacturing has emerged as a disruptive digital manufacturing technology. However, its wild adoption in the industry is still impacted by high entry challenges of design for additive manufacturing, limited materials library, processing defects, and inconsistent product quality. Machine learning has recently gained increasing attention in additive manufacturing due to its exceptional data analysis performance, such as classification, regression, and clustering. This paper provides a review of the state-of-the-art machine learning applications in different domains of additive manufacturing.
3D printing is used for various medical applications, such as the manufacture of guides for surgical operations, custom medical instruments, and low-cost medical applications. In few of these studies that have been performed, the effect of sterilization on these parts has not been considered yet. The fused filament fabrication process (FFF), which is the most widely used today, is used for the making of these guides and instruments. One of the most used materials in the FFF process is polylactic acid (PLA) due to its ease of printing, however, this could be degraded with the sterilization processes by steam heat and dry heat and lose its dimensional accuracy and resistance, something required for medical applications. The purpose of this study is to determine the effects of the steam heat and dry heat sterilization processes on the mixture of PLA and hydroxyapatite (HA) to check whether this mixture can be used in medical applications that are not implantable in the human body. The percentage by weight of hydroxyapatite used is 5%. To study the effect of sterilization processes already mentioned, 3D specimens were printed for flexural, tensile, Shore Hardness and impact mechanical tests. Thermogravimetric analysis (TGA), Differential scanning calorimetry (DSC) and Dynamic mechanical thermal analysis (DMTA) tests were also performed. It is concluded that the blend of PLA and hydroxyapatite increases its resistance to temperature but decreases its mechanical characteristics.
A novel additive manufacturing technique has been developed in the Manufacturing Science Laboratory at Lehigh University.The technique utilizes an extrusion based 3D printer, which has the ability to regulate the areaof the polymer flow inside the extrusion head, thus, allowing precise control over shear rate applied to polymer melt. The controlled shear alters the melt rheology, which in turn controls the evolution of crystallinity in the printed parts. The temporal control of shear translates to spatial control of melt rheology. Thus, the localized evolution of molecular orientation and nucleation/crystallization kinetics as well as the mechanical and optical properties can be precisely controlled during the additive manufacturing process. In this research, a semi crystalline poly-lactic acid (PLA)was utilized to validate the developed technique of controlling the shear rate while printing. The confinement will induce shear on the polymer the degree of which can be controlled by the gap between the conical cavity and theconical extruder tip. The analytical modeling results indicate that this strategy can increase the induced shear rate. Preliminary experimental analysis validated an increase incrystallinity percentage up to 16%.
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