Band 40, Heft 3 - Special Issue: PPS2024 Conference - Selected Contributions; Guest Editor: Ana Isabel Ares-Pernas

Slot dies are crucial for shaping operations in flat-film and sheet extrusion processes. For die design and process analysis, modeling of the flow within the die is essential. Alongside computational fluid dynamics (CFD) simulations, network theory – a computationally less expensive, semi-numerical approach – is used to predict, for instance, the pressure-throughput behavior and residence time. In many applications, the products extruded consist of multilayer structures that are formed from stratified flows fed into the slot die. In the absence of explicit expressions for the die conductance of co-extrusion flows, the application of network theory has hitherto been limited to mono-extrusion die flows. In this work, application of network theory was extended to include non-Newtonian two-layer co-extrusion flows in slot dies with rectangular manifold channels. First, a hybrid flow-modeling approach was employed to derive generalized symbolic regression models that predict relevant flow parameters (e.g., pressure-throughput behavior and average shear rates per layer) for two-layer slit flows of power-law fluids. These analytical expressions were then integrated into the flow network. A local power-law approach was chosen to describe the rheological behavior of the melts, for which an additional iterative loop was implemented to evaluate the average shear rate per layer (i.e., representative shear rate) for each network element. Solutions using this extended network approach are both fast and accurate, as confirmed by validation against results from CFD simulations.

Ultra-high molecular weight polyethylene (UHMW-PE) fibers and bacterial nanocellulose (BNC) display exceptional mechanical properties alongside the outstanding tribological properties of UHMW-PE while showing unrestricted biocompatibility. For combining the intrinsic advantages of both materials, the present work demonstrates an approach that integrates the slurry-based laser fusion of PE-polyvinylpyrrolidone (PE-PVP) composites and the subsequent bacterial biosynthesis of nanocellulose. PE-PVP composites exhibiting a fraction of 10 % of UHMW-PE fibers were additively manufactured through the locally selective laser-based layer-wise evaporation and subsequent sintering of aqueous suspensions, yielding fiber composites with a water-soluble matrix. The in situ synthesis of bacterial nanocellulose exploits the gelling and dissolving of high-molecular PVP in aqueous media. By allowing for the infiltration of printed PE-PVP composites with nanocellulose-producing Komagataeibacter xylinus , a multiscale composite of polyethylene fibers and bacterial nanocellulose was obtained, corroborating the infiltration of micrometer-scale PE fibers with nanoscale cellulose fibers. Release experiments using methylene blue confirmed the potentials of PE-BNC composites for drug delivery applications, showing first order sigmoidal release kinetics.

Friction stir welding (FSW) is a well-established method for joining metal components in lightweight construction. Despite its excellent joining properties, the process has primarily been used in high-tech applications requiring exceptional joint strength. However, the unresolved challenge of gap bridging limits its wider adoption. Recently, efforts have been made to extend FSW to plastic components. To address some of its limitations, this study presents a hybrid approach for thermoplastic materials, combining FSW with additive manufacturing techniques. In this method, a filament serves as a filler material during the welding process. This approach aims to improve both the mechanical and aesthetic properties of the weld seam by incorporating reinforced plastics, while also addressing the gap-bridging requirements of industrial applications. Newly developed stirring tools enable precise delivery of filler material into the process zone. Their modular design allows for the conveyance of both molten and solid material into the joining zone, with the conveying capacity adjustable through modifications to tool geometry. The results of joints made from various homogeneous plastic types, including ABS, PC, and PA6, are presented. The quality of the weld seams is assessed through mechanical characterization, complemented by microscopic examination to evaluate weld integrity.

In this contribution, the use of upcycled filaments for the functionalization of technical thermoformed components using additive manufacturing is investigated. In particular, small series applications in the automotive sector are being addressed. A high-quality plastic filament is obtained from the off-cuts of the thermoforming process and used in a robot-based process to print on and thus functionalize the thermoformed components. This involves adapted path planning, which allows printing on multi-axis curved free-form surfaces with an articulated robot. The resulting process combination of thermoforming and additive manufacturing offers a circular material flow and allows a reduction in waste and total energy consumption while simultaneously increasing the possible shape variety and component complexity. This creates an alternative to the injection molding process with its high tool costs, especially for small series and complex component shapes. In addition, the homogenization of the materials used has positive long-term effects on recyclability at the end of the product life cycle. In realizing the combined process, the adhesive bond of the printed structures to the thermoformed base component is particularly important for the mechanical properties of the finished part. If the bond at the interfaces is insufficient, forces cannot be transferred from functional elements like screw bosses, ribs and honeycomb structures to the base component and vice versa. Therefore, local thermal activation plays a crucial role in the 3D-printing process to ensure sufficient polymer chain movement and entanglement across the boundary layer.

Multi-layer materials are often used in technical parts to achieve certain properties of products and thus adapt their property profile to the respective application. However, a major challenge of plastics recycling is the separation of the various polymer components. One example for this are airbags. Airbags consist primarily of polyamide 6.6 (PA66) fibers and an additional silicone coating. During recycling, the silicone layer cannot be mechanically separated from the synthetic fiber. Therefore, the silicone particles remain in the recyclate with a poor bonding to the PA66 matrix. In turn, this can lead to poor mechanical properties of the recycled material and early material failure due to interface detachment. In this work, a new recycling strategy for the functional integration of silicone particles is demonstrated using the example of airbag waste. A reactive compounding of the waste with special coupling agents in a twin-screw extruder was conducted to achieve adhesion or coupling between the silicone particles and the PA66 matrix. Rheological tests confirmed the formation of physical or chemical bonds by adding the coupling agent, respectively. Nano-IR-AFM analyses demonstrated the improved integration of the silicone particles into the PA66 and the reduction of cavities in the compound. Mechanical characterization showed the increase in notched impact strength and elongation at break by almost double, which means that a higher plastic deformation is possible. These results show that coupling of the silicone particles to the PA66 matrix can be achieved by the use of coupling agents. The silicone particles no longer act merely as a filler, but as a functional additive or an impact modifier.

In previous studies, the synthesis of conductive PEDOT particles was carried out to incorporate them into thermoplastic matrices without relying on commercial PEDOT:PSS solutions or carbon- or metal-based additives. While promising conductivities were achieved, a high filler content was required to reach the percolation threshold. To reduce this filler content, the particle form factor appears to play a crucial role. To investigate this effect, rod-like particles were synthesized via supported polymerization of EDOT. Subsequently, thermoplastic composites were produced through melt-extrusion, blending PEO as the thermoplastic matrix with PEDOT-based fillers, resulting in a conductive thermoplastic material strip. The present study focuses on the synthesis of silica nanoparticles using the Stöber method, followed by the supported polymerization of EDOT to obtain electrically conductive silica@PEDOT nanoparticles. A comparison of percolation curves and percolation thresholds for three different fillers, exhibiting low to high form factors, was performed. Additionally, relationships between processing conditions, morphology, and material properties were analysed using SEM, TEM, TGA, and four-probe resistivity measurements.

Better injection molded part quality can be achieved with a more balanced filling of the single mold cavity. Furthermore, a more homogeneous cavity pressure distribution results in less overpacked regions contributing to an overall lower pressure difference between gate locations and end of fill regions (Lam, Y. and Seow, L.W. 2000). Different process design parameters can affect the melt filling pattern inside the cavity, most notably the gate location, injection speed as well as geometrical constrains and accelerators, known as flow deflectors and flow leaders, respectively. However, determining its geometric parameters such as path, length, and cross-section is not a straightforward task. To address this problem, we developed an automated flow leader generation routine that uses injection molding simulations to determine the longest flow path along which the thickness of the part will be gradually increased, ultimately reducing the overall melt flow resistance. Our flow leader generation approach is based on the work of Seow, L. and Lam, Y.C. (1997) and Lam, Y. and Seow, L.W. (2000). However, in contrast to the principle used by Lam and Seow, the thicker portion of our flow leader is near the injection location, thus exploiting the higher injection pressure. We applied our approach to a demonstrator part to keep track of the fill pattern improvement. The highest fill time difference on the part’s boundaries, called fill time delta, was chosen as a measure for the cavity balance. To validate our method, an injection molding tool for the demonstrator part was manufactured and experiments were performed. We manufactured one unmodified cavity and one flow leader cavity generated with our method. Finally, we were able to demonstrate that our automated flow leader generation method improved the cavity balance both in simulation as well as in experiments, while simultaneously reducing the maximum injection pressure.

Injection molding is a highly intricate manufacturing process where the design of the mold plays a pivotal role in mitigating defects such as incomplete parts, flash, sink marks, weld lines, air bubbles, warping, and shrinkage. Effective mold design, particularly during the cooling phase, is paramount for achieving high-quality parts and minimizing cycle time. However, optimizing the cooling phase remains challenging due to the extensive number of variables and their interdependent effects. This study addresses these challenges by focusing on the optimization of conformal cooling channels (CCCs), an advanced cooling technology engineered to significantly improve thermal efficiency and temperature uniformity compared to conventional cooling methods. The research utilizes advanced numerical simulation tools, such as Moldex3D, to assess the performance of CCCs and explore their potential to enhance injection molding outcomes. A key obstacle in optimizing CCCs is the multi-objective nature of the design problem, which initially involves 34 performance criteria, such as temperature gradients, cycle time, and defect rates. To manage this complexity, the primary aim of this study was to reduce the number of objectives while preserving the core attributes of the problem. Principal Component Analysis (PCA) was employed to condense these 34 objectives into four principal metrics, facilitating a streamlined and computationally efficient optimization process. To solve the reduced optimization problem, a suite of Artificial Intelligence (AI) methodologies, including data mining, Artificial Neural Networks (ANNs), and Multi-Objective Evolutionary Algorithms (MOEAs), was deployed. A case study involving a coffee-cup part validated the approach, demonstrating that CCCs achieved substantial improvements in temperature uniformity, defect reduction, and cycle time minimization. The findings underscore the efficacy of integrating AI-driven optimization with numerical modeling, highlighting the transformative potential of CCCs as a state-of-the-art solution for advancing injection molding processes.

Fully intermeshing co-rotating twin-screw extruders are used in various applications ranging from polymer to food processing. Since this extruder type exhibits excellent mixing and sufficient conveying behaviors, it is perfectly suited to use in applications that are demanding in terms of homogeneity, gentle material processing and high product quality. Tailoring the screw configuration and processing conditions to the input material requires accurate prediction of the extruder conveying and power-consumption behaviors. For this purpose, we present novel models of double-flighted fully intermeshing co-rotating conveying elements, which – due to their excellent conveying and pressure build-up capabilities – are the most commonly used elements in co-rotating twin-screw extruders. Our isothermal Newtonian models are based on computational fluid dynamics (CFD) simulation data of the complex element geometry without simplifications, from which we selected a subfactorial dataset of 772 design points that spans a broad parameter range, including various screw pitches, diameter ratios, and screw and barrel clearances. With this dataset as input, symbolic regression generated easy-to-use mathematical functions that incorporate the knowledge gained from the CFD simulations. For the first time, it is therefore possible to predict the conveying and power-consumption behaviors of fully intermeshing co-rotating conveying elements without any simplifications to the geometry and thus with higher accuracy. Our regression models combine the low application barrier of analytical models with the high accuracy of CFD simulations. Inclusion of the screw flight clearances additionally allows significantly improved prediction and understanding of the influences of screw wear on the conveying and power-consumption behaviors and thus on possible material degradation or process changes.