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.
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:
Plastic manufacturing can be unpredictable. Deviations in material batches, moisture content, machine calibration, among other variables, lead to issues in manufacturing quality and final part properties. This webinar will introduce how dielectric analysis (DEA) sensors be used to directly measure material behavior in-mold. New technology has been developed to combine dielectric analysis with machine learning and material models, allowing for dynamic adjustments to machine settings, removing uncertainty from your process, and optimizing cycle times. The material covered will include:
A new injection molding processing strategy called iMFLUX is becoming popular. iMFLUX is a low constant pressure process for filling and packing the part. Commercial injection molding simulation software traditionally is not designed for this process. However, you can simulate it. This paper will show how to set up and run simulations using currently available simulation software. Validation work of simulation work is also discussed.
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.
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.
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.
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.
Dimensional accuracy is – to this day – a challenging key quality aspect for manufactured parts using primary shaping processes. Many high-precision parts undergo multiple correction loops during the mold-making process to meet the required geometric tolerances. Each iteration not only increases production costs, but also requires additional human and environmental resources. Dimensional inaccuracies are caused by the process-based part deformation, a superposition of the phenomena shrinkage and warpage. The mechanisms for shrinkage and warpage a re strongly rela ted to the dependence of a polymer’s specific volume on pressure, and temperature (pvT-behavior). Shrinkage is inevitable, since it is caused by the continuous decrease in melt temperature and the inherent crystallization process during solidification. Warpage results due to local and timedependent variations of the melt temperature, cooling rate, and pressure and thus the local specific volume. These variations cause inner stress distributions that ultimately cause the part to warp. In order to reduce part warpage, the volumetric shrinkage must be homogenized across the part, which can be realized by manipulating the part's temperature locally and time dependent. In this work, the warpage of a box-shaped geometry with the material Polyoxymethylene (POM) is aimed to be reduced using a novel on-cavity thermally sprayed heating coating system. Two different heating coating systems are implemented and tested using commercial simulation and 3D-inspection software. For each system, the power level was varied, and the warpage was evaluated. In total, a warpage reduction of factor eight has been achieved using the heating coating.
The macro-issues the plastics industry is trying to resolve today pertain to sustainability, supply chain shortages, and the lack of skilled labor. Within the injection molding sector, manufacturers typically perform a full validation when a mold is moved to a different injection molding machine (IMM) or there is a material change. These full validations are labor-intensive, expensive, and not sustainable. Moreover, these methods may or may not utilize scientific molding principles. There has been a demand for a standard “part process” development method to transfer a mold between IMMs that is more efficient and can embrace variation in resins. iMFLUX’s Auto-Viscosity Adjust (AVA) technology has made doing so easier with its low, constant pressure injection molding process. This adaptive technology enables the molding process to automatically adjust parameters in real-time around parts’ response. This research focuses on developing a regenerative part process with low, constant pressure that is independent of resin and machine. Using AVA and cavity pressure sensors, two molds’ processes were transferred to another capable press with the original process, no user adjustments, and parts were studied for visual and dimensional integrity. It was determined that iMFLUX can automatically regenerate optimized part processes in different IMMs deemed capable with negligible part variation as seen from the visual and dimensional results. This is the first time an intelligent controller can autonomously redevelop and validate a part process to mold parts within spec despite varying IMMs and resins.
Glass-filled engineering polymers are a staple in the automotive, aerospace, and medical industries. However, understanding the influence of glass fibers on the crystallization behaviors of these polymers is not trivial - especially at heating and cooling rates encountered during melt processing. Sample preparation plays an important role in the success of thermal analysis. For conventional differential scanning calorimetry (DSC) characterization, larger sample size is often used for composites than neat resin to mitigate the impact of possible filler inhomogeneity. Because the sample is quite small (thickness of less than 20 µm and a mass of less than 1000 ng) for fast scanning calorimetry (FSC), the potential impact of sample preparation on data reliability is very significant. In this study we explore and quantify the effect of sample preparation for DSC and FSC on crystallization kinetics in a wide temperature range using three grades, Poly(ether ether ketone) (PEEK), and its glass-fiber-filled composites (PEEK with 15 wt% and 30 wt% glass fiber) were studied, and X-ray computed tomography (XCT) with an ultrahigh-resolution was performed to reconstruct the interior structure of the composite pellets. When the sample thicknesses is less than 50 µm, the PEEK sample sliced perpendicular to the fiber flow direction have good filler homogeneity and always have a lower coefficient of variation than pellets sliced along the fiber flow direction. Furthermore, the data collected by both FSC and DSC were fitted and showed that FSC and DSC analysis can be reasonably used to predict the kinetics of composite materials.
In injection molding of fiber-reinforced thermoplastics, in the presence of physical obstacles, such as cores, or for geometries that require multiple gates, weld lines develop where flow fronts rejoin. Regions affected by the presence of weld surfaces show worse mechanical properties. In fact, these areas are characterized by incomplete welding between the flow fronts and the presence of undesirable inclusions and porosity. In addition, due to the fountain-like flow in cavities, fibers on the weld line are unfavorably arranged and are unable to reorient themselves in the flow direction. If the incident flow fronts exhibit no pressure gradient during the defect formation and the holding phase, the morphology of the weld surface remains unchanged until the end of the process. By inducing a pressure imbalance after the formation of the weld line, on the other hand, it is possible to promote the interpenetration of one front into the other and significantly modify the local morphology. A dynamic packing stage during the first part of the holding phase therefore allows for improved matrix interdiffusion at the interface and fibers reorientation in the flow direction. Gas Assisted Injection Molding (GAPP) is a novel technology that allows for the dynamic packing of weld lines using only a single injection unit. Thanks to miniaturized gas injectors, it is possible to manipulate the molten polymer in the cavity and generate a flow through the weld surface. The dynamic packing achieved using GAPP allows for the elimination of weld lines in the core layer of the molded part, significantly increasing its mechanical performance. For a 35% glass fiber reinforced polypropylene, an increase in tensile strength and stiffness of 240% and 21.5%, respectively, can be observed in the defect region. GAPP can be implemented to solve weld line strength problems in all parts made of fiber-reinforced thermoplastics that require high mechanical performance, such as supports, brackets, cooling fans, pulleys, and other structural parts.
In this work, a practical and simulative study of the surface roughness of the injection mold cavity and the corresponding heat transfer between the plastic melt and the mold cavity was conducted. The work shows that slightly longer flow paths can be achieved through the rougher mold surface, which indicates to a lower heat transfer. However, the influence is small compared to other influences such as the used molding compound or the injection pressure. An analysis of the structural replica over the flow path shows a clear decrease in the structural height towards the end of the flow path, i.e. with decreasing pressure and lower melt temperature. To describe the influence of microstructures on the heat transfer using injection molding simulations accurately, a model calibration is used.
Injection molding simula tion bega n in the 1970’s, with technology advancing to the present day. In parallel, material testing technology has evolved over the same period, to meet the increased accuracy demands of simulation. The accuracy of injection molding simulation is influenced by many factors such as: (i) modeling of part geometry, (ii) runner and nozzle, (iii) mesh type and density, (iv) mathematical finite element solution, (v) injection molding machine process settings, and (vi) material data. The focus of this paper is material data, from the latest material testing methodologies, outlining technological evolution to meet the demands of the highest accuracy simulation.
Co-injection molding has been introduced into industrial application for several decades. However, due to the formation of the interface between skin and core materials is very difficult to be observed, and controlled, a good quality of co-injection product can not be obtained effectively. The reason is that the formation of that interface in co-injection molding is very sensitive to various factors. In this study, the formation of the interfacial morphology and its physical mechanism in coinjection molding have been studied based on the ASTM D638 TYPE V system by using both numerical simulation and experimental observation. Results showed that the critical skin/core material ratio to generate the skin breakthrough is identified. The reason to cause the breakthrough is due to the flow front of core material catches up with the melt front of skin, and the skin is stop at a fixed distance. This mechanism is similar with that of literature. However, when the higher core material ratio is selected, the mechanism of the interfacial morphology is different. Specifically, after core melt front catches the skin melt front, the broken skin material can move forward with the inner core material to generate special core-skin-core structure. It could be due to different forces balance inside the skin and core melts, but needs to do more study in the future.
Precise predictive models are required for the use of machine learning methods for quality control in injection molding. Thermal images offer the advantage of containing information in the data that is not available in machine and process data. Currently, convolutional neural networks (CNN) have numerous applications in image recognition. Therefore, the objective of this work was to investigate the application of convolutional neural networks to thermal images of injection molded parts. For this purpose, 751 injection molding cycles from a central composite design were used. The goal was to predict the weight, height, and width of the injection molded part. The results were also compared with classical machine learning methods. Depending on the quality parameters, the networks were able to achieve an R² of up to 0.91 and were thus among the three best methods.
Nonlinear warpage analysis which considers different kinds of nonlinearity effects has attracted more and more attention recently, especially in the automotive industry. This study is mainly aimed at using the new functions in Moldex3D, “Nonlinear warp analysis” and “Buckling analysis”, to predict the warpage of the products. These new solvers cooperate with the temperature distribution and the residual stress caused by the phase change from the manufacturing process and predict the deformation of the product considering the geometric characteristic and process conditions.
Injection molding is one of the most popular techniques for global plastic production. With this automation technique, the plastic products can be manufactured at low cost with a complex geometrical shape. A manufacturing process with high productivity of an injection molding machine depends on optimized injection molding parameters. Injection molding pressure and temperature inside the mold cavity are the most critical parameters. However, cavity pressure transfer is not used due to cost and maintenance issues. During this research, an experimental procedure is performed to determine a process monitoring system using asynchronous data acquisition, through the incorporation of a wired piezo-ceramic sensor to acquire pressure of the injection molding system. This piezoelectric sensor is designed in such a way that, a Bluetooth device can be connected with a sensor and can take live data reading of parameters from the running molding machine.
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