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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In this work, polypropylene (PP) reinforced with cellulose filaments (CF) nanocomposites were studied. Nanocomposites with CF loadings ranging from 0 to 30 wt% were produced by melt extrusion and characterized. Rheology using Carreau-Yasuda with yield stress model was used to estimate the dispersion state of CFs and showed that a suitable dispersion was achieved. Tensile tests were conducted to study the mechanical behavior of the materials. Results showed that nanocomposites with a higher rigidity can be obtained when a suitable dispersion is achieved, however, those nanocomposites are consequently more brittle.
In recent years, injection molding has become a common polymer molding process in the plastics industry. Injection molding can produce products which have complicated shapes, and offers high efficiency and productivity with low cost. However, uneven volume shrinkage during the injection molding process will cause deformation after demolding and poor product roundness. To find the best approach, this study focused on round products under different molding conditions, including melt temperature, mold temperature, and injection speed. CAE simulation was used to identify the effect of different fiber content and molding conditions on product roundness. Results showed that the roundness of PC material products without fibers was better than those with fibers. Moreover, those which contained more fibers exhibited lower dimensional stability. The uneven shrinkage and variations in roundness were caused by the different fiber orientations in the product. Changing the position of the gate improved the uneven shrinkage caused by fiber orientation. Furthermore, raising the mold temperature also improved the roundness of the product.
Discontinuous fiber reinforced thermoplastics materials combine the process related advantages of injection molding with the enhanced mechanical properties of fiber-reinforced plastics. Since the mechanical properties of FRPs are dependent on the fiber length, long fiber reinforced thermoplastics (LFT) offer a huge potential for lightweight design. In order to analyze the influence of the fiber length and orientation on the stiffness and strength of LFT under high strain rate loading, the authors conducted tensile impact tests as well as part impact tests. Thereby, polypropylene (PP) specimens with different fiber length were analyzed. It can be shown that the influence of the fiber length on the strain rate behavior can be neglected compared to the influence of the fiber orientation. Furthermore, a first implementation to describe the strain rate-dependent material behavior of LFT is presented.
There is an often ignored correlation between mechanical and chemical stress in plastics parts, which is very time- and cost-intensive to test. The model that was developed in this research allows the prediction of the mechanical properties of fiber-reinforced amorphous thermoplastics under media influence. The media influence is taken into account by the modulus of elasticity of the matrix, which is determined by a molecular dynamics simulation. The results correspond well with the tests for polycarbonate after storage in distilled water. An acoustic emission analysis shows an influence of the medium on the damage behavior of the fiber-reinforced samples during the test. The media storage results in significantly more acoustic emissions during the test. This indicates damage already during the test and not just shortly before the break.
The characteristics and the quality of a blown film are strongly influenced by the stretching and simultaneous cooling of the molten polymer within the bubble formation zone. Moreover, the output rate of the process is generally limited by the cooling rates and the stability of the bubble. As a consequence, the design of cooling systems is highly relevant in terms of process optimization. Beside intensifying the heat removal, the concept of secondary air cooling aims at eliminating unsteady ambient influences during bubble formation and increasing bubble stability. Different concepts for cooling systems with integrated secondary air cooling are presented and experimentally tested. The results confirm that the implementation of secondary air cooling is feasible and generally supports bubble cooling regarding the aforementioned intention. Furthermore, the study reveals that an accurate design of such cooling systems is required.
Ines Kuhnert, Matthieu Fischer, Michaela Gedan-Smolka, Peter Scholz, Didier Garray, March 2019
In this study, adhesive metal-polymer composites were investigated using pretreated aluminum substrates, each with an adhesion-promoting powder coat, a thermoplastic urethane elastomer (TPU) stress-compensation layer and a polyamide 6 top coat to allow further functionalization. One of the composite’s key features is the powder coating, which acts as a reactive adhesive agent and possesses a high-quality surface finish and very good formability. The composites were examined in terms of shaping and assembly behavior. The interfacial bonding was investigated using a peel test. Finally, a demonstrative part was manufactured for the automotive-interior sector.
Poly(methyl methacrylate- co- trimethoxysilyl)propyl methacrylate)(P(MMA-co-TMSPMA), and a chain extender, Joncryl ADR 4368 were used in this study. P(MMA-co- TMSPMA) copolymers were prepared by dispersion polymerization using a azo initiator with different TMSPMA content. The effect of the chain extender and/or P(MMA-co-TMSPMA) blends on the properties were investigated. The thermal and mechanical properties were analyzed with a differential scanning calorimeter, rheometer, and universal tensile machine. The crystallinity of the blends was found to be decreased while the complex viscosity increased. The results of the mechanical properties revealed that the addition of chain extender have an effect on increasing the tensile strength. Joncryl system increased molecular weight by forming a long chain branching structure for PLA blends. Also, it was found that the incorporation of the chain extender could enhance the degree of P(MMA-co-TMSPMA) dispersion.
Although modern injection molding machines allow operating processes resulting in high part quality and low reject rates, there are still external influences such as fluctuations in the material properties that may cause the production of reject parts. These are often detected with a time delay, which entails high costs.
Hence, quality forecasting or control based on process data would be desirable. However, existing approaches did not yet prevail in industrial applications for several reasons. Therefore, supervised machine learning approaches may not always be applicable.
Based on previous, univariate approaches aiming to overcome the limitations of the standard process monitoring with its fixed tolerance limits, this paper presents a procedure for multivariate anomaly detection in injection molding process data using cluster analysis as a means of unsupervised machine learning. The procedure allows detecting critical process states in real time and thus lays the foundation for root cause analysis and holistic process optimization.
In industry wear-prone components of injection molding machines are usually replaced by specified maintenance intervals. On the one hand, often these components are replaced too early, so the component service life is not utilized to full capacity. On the other hand a failure causes time-consuming and costly production downtimes. Thus, applications for predictive maintenance of wear-prone components are highly desired by injection molding machine manufacturers and users. Based on this, the present paper describes a procedure to use production planning data available in injection molding production to predict the service life of machine components, resulting in increased machine uptime while reducing storage and maintenance costs.
Besides the aerospace industry, endless carbon-fiber reinforced plastics have also spread towards many further applications such as automotive or civil engineering. Their superior strength and stiffness to mass ratio make them increasingly attractive high performance materials. In this work, we report on a Probability of Detection analysis (POD analysis) of an air-coupled ultrasound inspection of thermoplastic CFRP tapes. Several preliminary works were performed in order to optimize the data set being involved within the POD analysis. The influence of the tape temperature on the ultrasonic transducers and the behavior of the ultrasonic signal in thermoplastic tape at elevated temperature of up to 120 °C were examined in a specially developed testing rig. For maximizing the spatial resolution of ultrasonic transmission measurements, a cone design was developed. Special emphasis was given to both the achievable signalto-noise ratio (SNR) and the spatial resolution. The POD analysis was determined for artificial cuts through the thickness of the tapes and different fitting models were applied.
Clogging of venting slots in injection molds is a common maintenance problem related to the degradation of the resin and the accumulation of corrosive residues on cavity walls. In this work, the effect of laser-induced periodic surface structures on the self-cleaning properties of venting slots was investigated. The degradation of PET over different surfaces was characterized using a specifically designed experimental setup. The results indicate that the use of regular nano-structures aligned along the flow direction minimizes the deposition of gaseous residues on the venting slots.
Thermoset In-Mold Forming (Duro-IMF) is a new hybrid process to combine the molding of fiber reinforced plastics (FRP) and injection molding of thermosets in one single step. In contrast to the conventional processes, the new molding technology enables the joint curing of both com-ponents in one mold, thus reducing the cycle time and in-crease efficiency. This study deals with the behavior of the Duro-IMF process for a high filled molding compound. One of the biggest challenges is the combination of the highly differentiated processing specifications of the two components. High injection pressures are required to fill the cavity with the highly filled thermoset molding com-pound, whereas only very low pressures are required when processing prepregs with low-viscosity resin. Accordingly, there is a risk that the injection molding component causes overmolding at the prepreg next to the interface. This paper investigates the reduction of overmolding by compression of the prepreg in the prerpeg cavity and the resulting dis-placement of the prepreg into the injection mold cavity. The results are correlated with the resulting component properties such as bond strength and residual strength of the prepreg.
Microcellular injection molding (MIM) has been extensively used for producing foamed plastic parts. It has the advantage of material, energy, and cost savings, as well as enhanced dimensional stability. However, the insitu characterization of MIM is still challenging. In this study, an ultrasonic method for the real-time characterization of MIM parts parameters—i.e., cell size, surface roughness, and skin layer thickness—during the MIM process is proposed. To the best of our knowledge, this is the first time that ultrasonic technology has been employed for the characterization of MIM. A series of experiments were performed to validate the proposed method. Experimental results showed that the duration process times of the ultrasonic signals and the change of the ultrasonic speed in the transducer path could be used to characterize the cell size and the skin layer thickness.The time delay of the first ultrasonic signal and the largest ultrasonic amplitude of the ultrasonic signals was employed to characterize the surface roughness. The proposed method has the advantages of being nondestructive, real-time, low-cost, and safe. As such, it has significant application prospects in MIM production.
Injection molding of parts characterized by large overall dimensions is a challenging task due to the rise of cavity pressure during the filling phase. Process limitations lead to oversized design solutions and cavity thicknesses that are greater than the structural properties required for the part. In this work, a novel approach to thickness reduction is proposed exploiting the thermal-insulation effect of mold surface coatings. The effects of different mold surface coatings on the melt flow resistance of polyethylene terephthalate was experimentally characterized. Then, the thermal boundary condition of a numerical injection molding simulation was calibrated and it was used to quantify the thickness reduction associated to each coating. The results show that mold surface coatings can be used to improve the design of injection molded parts by reducing their wall thickness.
There is a long history of the use of ultra-high molecular weight polyethylene (UHMW-PE) in orthopedic implants, due to its excellent wear, friction, and impact properties. But the high molecular weight and extremely high viscosity of UHMW-PE limits the processing methods available. This paper examines the properties of a UHMWPE material with a viscosity that makes injection molding possible. We compare the injection molding performance of this material with a standard high density polyethylene, and compare the mechanical and wear properties of the material with conventional UHMW-PE. Additionally, we explore the effect of electron beam treatment on material properties.
Electrically induced mechanical stress was produced within polycarbonate(PC) monolithic films when subjected to an instantaneous DC voltage using a needle-plane electrode setup. The electrical induced mechanical deformation on PC surface was measured and characterized. It was found that a conical indentation was created upon the application of the field, and the depth and volume of the indentation increased as applied electric field increased. The loss energy was determined from the recorded charge-discharge current profile, and its relationship with the indentation will be discussed. A series of experiments on the application of instantaneous DC field with different durations between charge and discharge was carried out to study the effect of time on the plastic deformation. The results manifested that the indentation is a time-dependent deformation under a constant applied field, which is similar to creep phenomenon. Annealing treatment at elevated temperature was performed on the indentation and full recovery of the deformed volume was observed at temperature above the Tg of polycarbonate, indicating that the indentation is a mechanical deformation.
Jens Wipperfurth | Jonathan Alms | Christian Hopmann, March 2019
The quality of foams especially the foam structure or the porosity is often controlled using cameras and image processing tools. These approaches require an expensive sample preparation and a complex illumination to provide highly contrasted images and is therefore not suitable as an inline system. In addition, subjective parameters such as sharpness and contrast have to be adjust subjectively by trained employees and prevent a consistent quality assurance. A serious alternative are neural networks as they are able to analyze images very quickly and accurately without the need for high contrast images. In this paper we present a neural network that is able to classify low contrasted foam structures with a prediction accuracy of 90 %. On this basis an optical inline system is presented using the example of the value chain of a ceramic foam filter that ensures the quality of each filter across the entire value chain.
In this study, investigation of the effect of process parameters on fiber length, fiber orientation and tensile strength of long glass fiber reinforced polypropylene molding. In the study, the polypropylene composite material with glass fiber length of 25 mm was taken as the research object. The special long fiber injection molding machine was used to investigate the change of fiber quality, and the injection molding experiment was carried out with different screw speed and back pressure, and observation on the fracture condition of the fiber during the plasticization process, using this as the basis for the process parameters setting. A tensile test specimen mold with different melt flow path was designed to understand the effects of process parameters on tensile strength properties of long fiber injection molding. It can be found from the experimental results that in the fiber breaking length experiment using the three-stage with plunger mechanism of the special injection molding machine for long fiber reinforced thermoplastic composite molding, increasing the screw speed and the back pressure will shorten and break the fiber (retain 40~50% of the fiber length). The length of the fiber further affects the tensile strength of the molded specimen. On the other hand, as the melt temperature and mold temperature increase, it affects the fluidity and compatibility between melt and glass fiber, the tensile strength will have a relatively increased trend. From the microstructure observation showed that the tensile specimen closer to the gate has poor fiber diffusibility and affects the tensile strength of the molded specimen; moreover, the tensile specimen with double gate melt flow path resulted lower weldline strength.
A neural network has the advantages of high accuracy and fast speed in numerical prediction, and its disadvantage is that a large amount of training data is required for network training. There is a great variety of injection molding products, and the neural networks of various products cannot be shared directly. Therefore, each prediction module needs to take up a lot of time to make the training data, meaning that the prediction module cannot be applied to an actual injection molding mold trial. This research imported the concept of transfer learning to retrain the well-trained neutral networkarchitecture and hyperparameter according to the training data of similar products and to explore the effect of form and structure of training data on the accuracy of transfer learning. This research used 2 models of a circle plate and square plate to transfer the well-trained circle plate model to be used by the square plate. The research results showed that the Random Shuffle method for data pre-processing can improve the overfitting problem in addition to reducing the error rate of prediction. The prediction of complicated warpage is the most obvious. In the training of the circle plate, the error of gate warpage fell from 29.85% to 19.90%. When the Random Shuffle method is used in combination with the square plate model of transfer learning, the error rate of warpage also fell from 59.61 to 31.05. Keywords: Injection Molding, CAE, BPNN, Machine Learning, Transfer Learning
Powder metal “printed parts,” using 3-D additive manufacturing (AM) methods using selective laser sintering (SLS, also known as SLM for “Selective Laser Melting”) of metal powders run under argon, have been widely tried by many plastic injection molding die manufacturers with only limited success. Injection molds manufactured using 3-D powder bed SLS AM methods typically suffer from poor material properties (particularly low ductility and early fatigue crack failures) and porosity. As a result, SLS has only found limited usefulness as either a tooling design aide or for very small batch part prototyping programs.
Fundamental surface physics, associated with adsorbed air and water vapor bound to the surface of most commercially available powder metals is believed to be partly responsible for the formation of local oxides on the surface of the grain boundaries formed during SLS performed under argon atmospheres.
The overlay of multiple layers of thin oxide films formed repeatedly on the grain boundary surfaces of SLS sintered structures substantially contributes to parts having lower part ductility and reduced fatigue properties. SLS printed parts typically become much more brittle and are subject to premature cracking. The “in-situ” oxide inclusions formed also contribute to both the low part density and hermeticity issues experienced with 3-D AM printed components, but local variations in the printing technique still drive most of the structural porosity issues being experienced.
Traditional injection molding dies fabricated using wrought materials that are diffusion bonded using the “Laminated Object Manufacturing” (LOM) method do not suffer from the same uptake of oxygen at the grain boundaries during the liquid interface diffusion (LID) bond joining of the mold body. As a result, these dies do not suffer from the low ductility and early fatigue life failures seen with SLS printed injection molding dies, and often have fatigue lives three orders of magnitude higher than for parts SLS printed under argon.
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