The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning model. Given the material, screw geometry, shot weight, and desired plasticizing time, the model is able to predict the back pressure and screw rotational speed required to achieve good melt quality. We show how data sets can be pre-processed in order to obtain a generalized model that performs well. Various supervised machine learning algorithms were compared, and the best approach was evaluated in experiments on a real machine using the predicted basic machine settings and three different materials. The neural network model that we trained generalized well with an overall absolute mean error of 0.27% and a standard deviation of 0.37% on unseen data (the test set). The experiments showed that the mean absolute errors between the real and desired plasticizing times were sufficiently small, and all predicted operating points achieved good melt quality. Our approach can provide the operators of injection molding machines with predictions of suitable initial operating points and, thus, reduce costs in the planning phase. Further, this approach gives insights into the factors that influence melt quality and can, therefore, increase our understanding of complex plasticizing processes.

Composite filament co-extrusion (CFC) additive manufacturing (AM) is a bi-matrix rapid fabrication technique that is used to produce highly customisable composite parts. By this method, pre-cured, thermoset-based composite carbon fibre (CCF) is simultaneously extruded along with thermoplastic (TP) binding melt as the matrix. Like additive manufacturing, CFC technology also has inherent challenges which include voids, defects and a reduction in CCF’s volume in the fabricated parts. Nevertheless, CFC AM is an emerging composite processing technology, a highly customisable and user-oriented manufacturing unit. A new TP-based composites processing technique has the potential to be synergised with conventional processing techniques such as injection moulding to produce lightweight composite parts. Thus, CFC AM can be a credible technology to replace unsustainable subtractive manufacturing, if only the defects are minimised and processing reliability is achieved. The main objective of this research is to investigate and reduce internal voids and defects by utilising compression pressing as a rapid consolidation post-processing technique. Post-processing techniques are known to reduce the internal voids in AM-manufactured parts, depending on the TP matrices. Accordingly, the rapid consolidated neat polylactic acid (PLA) TP matrix showed the highest reduction in internal voids, approximately 92%. The PLA and polyamide 6 (PA6) binding matrix were reinforced with short carbon fibre (SCF) and long carbon fibre (LCF), respectively, to compensate for the CCF’s fibre volume reduction. An increase in tensile strength (ca. 12%) and modulus (ca. 30%) was observed in SCF-filled PLA. Furthermore, an approximately 53% increase in tensile strength and a 76% increase in modulus for LCF-reinforced PA6 as the binding matrix was observed. Similar trends were observed in CFC and rapidly consolidated CFC specimens’ flexural properties, resulting due to reduced internal voids.

Many theoretical analyses of extrusion ignore the effect of the flight clearance when predicting the pumping capability of a screw. This might be reasonable for conventional extruder screws with “normal” clearances but leads to errors when more advanced screw designs are considered. We present new leakage-flow models that allow the effect of the flight clearance to be included in the analysis of melt-conveying zones. Rather than directly correcting the drag and pressure flows, we derived regression models to predict locally the shear-thinning flow through the flight clearance. Using a hybrid modeling approach that includes analytical, numerical, and data-based modeling techniques enabled us to construct fast and accurate regressions for calculating flow rate and dissipation rate in the leakage gap. Using the novel regression models in combination with network theory, the new approximations consider the effect of the flight clearance in the predictions of pumping capability, power consumption and temperature development without modifying the equations for the down-channel flow. Unlike other approaches, our method is not limited to any specific screw designs or processing conditions.

Co-extrusion is a widely used processing technique for combining various polymers with different properties into a tailored multilayer product. Individual melt streams are combined in a die to form the desired shape. Under certain conditions, interfacial flow instabilities are observed; however, fundamental knowledge about their onset and about critical conditions in science and industry is scarce. Since reliable identification of interfacial co-extrusion flow instabilities is essential for successful operation, this work presents in situ measurement approaches using a novel co-extrusion demonstrator die, which is fed by two separate melt streams that form a well-controlled two-layer co-extrusion polymer melt flow. An interchangeable cover allows installation of an optical coherence tomography (OCT) sensor and of an ultrasonic (US) measurement system, where the former requires an optical window and the latter good direct coupling with the cover for assessment of the flow situation. The feasibility of both approaches was proven for a material combination that is typically found in multilayer packaging applications. Based on the measurement signals, various parameters are proposed for distinguishing reliably between stable and unstable flow conditions in both measurement systems. The approaches presented are well suited to monitoring for and systematically investigating co-extrusion flow instabilities and, thus, contribute to improving the fundamental knowledge about instability onset and critical conditions.

In this study, the influence of curvilinear fibre reinforcement on the load-carrying capacity of additively manufactured continuous carbon fibre reinforced necked double shear lugs was investigated. A curvilinear fibre placement is descriptive of layers in extrusion-based continuous-fibre-reinforced additive manufacturing with carbon fibres aligned in the directions of principal stress. The alternating layered fibre trajectories follow the maximum and minimum principal stress directions due to axial tension loading derived from two-dimensional finite element analysis (FEA). The digital image correlation was utilised to monitor the strain distribution during the application of tensile load. The 2D FEA data and the tensile test results obtained were comparable, the part strength and the linear approximation of stiffness data variability were minimal and well within the acceptable range. Nondestructive fractography was performed by utilising computed tomography (CT) to analyse the fractured regions of the tensile-tested lug. The CT scanned images aided in deducing the failure phenomenon in layered lugs; process-induced voids and fibre layup undulation were identified as the cause for lug failure.

Due to progress in the development of screw designs over recent decades, numerous high-performance screws have become commercially available in single-screw extrusion. While some of these advanced designs have been studied intensively, others have received comparatively less attention. We developed and validated a semi-numerical network-theory-based modeling approach to predicting flows of shear-thinning polymer melts in wave-dispersion screws. In the first part (Part A), we systematically reduced the complexity of the flow analysis by omitting the influence of the screw rotation on the conveying behavior of the wave zone. In this part (Part B), we extended the original theory by considering the drag flow imposed by the screw. Two- and three-dimensional melt-conveying models were combined to predict locally the conveying characteristics of the wave channels in a discretized flow network. Extensive experiments were performed on a laboratory single-screw extruder, using various barrel designs and wave-dispersion screws. The predictions of our semi-numerical modeling approach for the axial pressure profile along the wave-dispersion zone accurately reproduce the experimental data. Removing the need for time-consuming numerical simulations, this modeling approach enables fast analyses of the conveying behavior of wave-dispersion zones, thereby offering a useful tool for design and optimization studies and process troubleshooting.