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.

Highly filled rubber compounds exhibit a unique rheological behavior, which is affected by its filler–filler and filler–matrix interactions leading to pronounced nonlinear viscoelasticity. The necessity to consider these characteristics in rheological testing and modeling, adds further complexity providing universally valid numerical descriptions. In the present study, the pressure driven contraction and capillary flow of a carbon black filled hydrogenated acrylonitrile–butadiene rubber compound is studied both experimentally and numerically. Rheological testing indicates no pronounced slippage at the wall but a shear sensitive plug flow at the centerline. The viscoelastic Kaye‐Bernstein–Kearsley–Zapas/Wagner, the viscoplastic Herschel–Bulkley and the viscous power‐law models are used in computational fluid dynamic simulations aiming to predict measured pressure drops in an orifice and various capillary dies. Viscoelastic modeling is found of particular importance describing contraction flow dominated areas, whereas viscous models are able to predict pressure drops of capillary flows well. POLYM. ENG. SCI., 60:32–43, 2020. © 2019 The Authors. Polymer Engineering & Science published by Wiley Periodicals, Inc. on behalf of Society of Plastics Engineers.

Easy and residue‐free demolding is an everlasting topic in the plastics processing industry. Typically, facile ejection of the produced parts from the mold is provided by separation agents (silicon sprays, surface coatings). In this work, a perfluoroalkyl‐based organosilane coating is applied to exchangeable substrates of an injection mold. Besides the simple application, the coating can also be restored easily in a procedure based on flame treatment. Coating and recoating are proven by contact angle measurements with water, while the anti‐adhesive effect and the related relief during demolding are evaluated using a special measuring device in an instrumented two‐plate injection mold. The results reveal that the organosilane layer reduces the demolding forces and the resulting static friction coefficient by 50%. Furthermore, multiple recoating significantly improves the durability of the anti‐adhesive coating. Based on these findings, the easily applicable and renewable organosilane coating represents a suitable alternative to conventional release coatings.

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

In the co‐extrusion of plastics, pressure‐throughput behavior, layer distribution, and residence time are crucial parameters, modeling of which contributes to manufacturing high‐quality products at optimized process efficiency and significantly shortens development times for die systems. In previous work, we have presented symbolic regression models to predicting the (i) pressure‐throughput behavior, (ii) position of the interface, (iii) interfacial shear stress, (iv) ratio of volume flow rates, and (v) interfacial flow velocity for isothermal two‐layer co‐extrusion flows through rectangular ducts. These regression models are mathematically simple and capable of capturing the shear‐thinning nature of polymer melts without the need for numerical methods. Here, we present an experimental study validating the proposed models against co‐extrusion process data and comparing them to existing theories. To this end, a two‐layer co‐extrusion demonstration die instrumented with an optical coherence tomography sensor for detecting the interfacial position was used. To accurately set up and evaluate the die flows, the overall co‐extrusion process was represented by means of a digital process twin. Industrially relevant combinations of materials were tested under a wide range of processing conditions. Comparisons of pressure losses and interfacial positions to the predictions showed excellent agreement and the results outperformed the concept of representative viscosity.