Intermeshing co‐rotating twin‐screw extruders are very versatile because their screw configurations can be tailored both to the application and to the properties of the materials used. Finding the best screw configuration is one of the main purposes of twin‐screw extrusion modeling, and requires models that accurately predict conveying and power consumption. The better the process can be predicted, the better the requirements of the final product can be met. We present novel prediction models of the conveying and power‐consumption behaviors of intermeshing co‐rotating twin‐screw extruder kneading blocks for Newtonian fluids. These are based on numerical simulations and therefore consider the complex three dimensional (3D) geometry of this element type without the need for common simplifications. Our models are thus capable of including all leakage flows and gap influences, which are usually ignored, for example, by the flat‐plate model. Since our models are derived by symbolic regression based on genetic programming, they consist of algebraic functions and are low‐threshold. They can be used to calculate various process parameters for individual kneading blocks or entire screw configurations, as illustrated by a use case.

Mathematical models of polymer melt flow in co‐rotating twin‐screw extruders are crucial to screw design and predict processing characteristics, such as pressure distribution, back‐pressure lengths, degree of filling, melt‐temperature increase, and drive power. Twin‐screw modeling focuses predominantly on conveying elements, and kneading blocks are commonly represented with fictitious continuous flights, which significantly simplifies geometry and ignores considerable leakage flow. This work (Part A) presents a comprehensive analysis of the conveying characteristics and power demands of fully intermeshing co‐rotating twin‐screw extruder kneading blocks that considers the complex three‐dimensional geometry without geometrical simplifications. This analysis comprises the following steps: (1) dimensionless description of the geometry, (2) simplification of the governing equations, (3) formulation of novel dimensionless conveying and power parameters, and (4) a parametric design study with the novel approach of using the characteristic angular screw position, which avoids complex numerical algorithms and drastically reduces the computation required. Our comprehensive parametric design study included 1536 independent design points—a vast amount of data that revealed various effects that are highlighted in this work, including new findings on the interactions between geometry and conveying and power parameters. The obtained results serve, for example, as the basis for screw design, optimizations, scale‐up, and soft sensors.

Modeling of the transport phenomena in fully intermeshing co-rotating twin-screw extruders is key to predicting their processing characteristics, and is commonl

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.

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.

Modeling and simulation of the melt conveying of co‐rotating twin screw extruders, one of the important manufacturing machines in polymer processing, is commonly done by means of generalized, dimensionless models. Recently, novel general hybrid models have been published, which combine physics‐based approaches with extensive simulation and machine learning. This article experimentally validates the pressure‐throughput behavior prediction of these models for four different polymer grades, three screw element geometries and a large range of extruder setpoints. An automated data processing evaluates the data, resulting here in an average prediction error of 7.3%, and showing a close agreement between the models and the measurements. However, subsequent uncertainty analysis shows that large measurement noise raises the mean uncertainty of the processed experimental data to 14.5%. Furthermore, we show that the experimental results exhibit large sensitivity to fluctuations in viscosity, which is further very sensitive to temperature and assumes a constant representative shear rate. Most operating points suffer from having a wide‐range temperature distribution across the melt channel, which is shown by numerically finding representative cup‐mixing temperature using the Monte Carlo approach. Despite these limitations, the results demonstrate the hybrid models to agree well with experimental data and outperform existing analytical modeling frameworks.